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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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Engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

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computers, Materials & Continua 2023年第5期75卷 2727-2740页

作者： Maryam Alhassan Abdul Raouf Khan Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityAlahssa31982Kingdom of Saudi Arabia

Preserving privacy is imperative in the new unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)architecture to ensure that sensitive information is protected and kept secure throughout the ***,efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource *** study proposes a lightweight and efficient authentication scheme for *** proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardwarebased solution with a simple *** proposed scheme works in four stages:system initialization,EU registration,EU authentication,and session *** is implemented as a single hardware chip comprising registers and XOR gates,and it can run the entire process in one clock ***,the proposed scheme has significantly higher efficiency in terms of runtime and memory consumption compared to other prevalent methods in the *** are conducted to evaluate the proposed authentication *** results show that the scheme has an average execution time of 0.986 ms and consumes average memory of 34 *** hardware execution time is approximately 0.39 ns,which is a significantly less than the prevalent schemes,whose execution times range in ***,the security of the proposed scheme is examined,and it is resistant to brute-force *** 1.158×10^(77) trials are required to overcome the system’s security,which is not feasible using fastest available processors.

关键词： MEC UAVs drones security efficiency authentication hardware-based solution password-less authentication

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Credit card fraud detection using hybridization of isolation forest with grey wolf optimizer algorithm

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Soft Computing 2024年第17-18期28卷 10215-10233页

作者： Tabrizchi, Hamed Razmara, Jafar Department of Computer Science Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit cards play an essential role in today's society, the misuse of these cards will lead to significant damages. One of the common ways to deal with these possible damages is using anomaly detection systems. These systems aim to take account of changes in customer and fraudsters’ behavior to detect anomaly patterns. In the current study, we present a model namely IF-GWO to learn fraudulent patterns through analyzing past transactions. The method employs a novel ensemble learning method using isolation forest (IF) and Grey Wolf Optimizer (GWO). The experimental results indicate the priority of our presented fraud-detection system based on a noticeable number of credit card account transactions. Compared to the conventional model used for anomaly detection, the proposed model can detect more fraud accounts with fewer false positives over comparative procedures. Based on a comparison with other models using the dataset contains 284,807 transactions that are made by European cardholders, the proposed model outperformed the other approaches and achieved the highest performance in terms of F-Measure (93.52%), Area under receiver operating characteristic curve (AUC) (94.17%), and G-means (94.10%). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Anomaly detection

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AGG: attention-based gated convolutional GAN with prior guidance for image inpainting

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Neural Computing and Applications 2024年第20期36卷 12589-12604页

作者： Yu, Xiankang Dai, Lei Chen, Zhihua Sheng, Bin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Image inpainting has made great achievements recently, but it is often tough to generate a semantically consistent image when faced with large missing areas in complex scenes. To address semantic and structural alignment in existing methods for image inpainting, this paper proposes an end-to-end attention-based gated convolution GAN with prior guidance named AGG, which designs the spatial and channel attention mechanisms for full extraction of semantic and structural features. Moreover, AGG constructs the attention-based upsampling module based on the channel attention to refine the feature map and capture more details from features of up-level low sizes. AGG uses the image contour as prior, allowing the gated convolution and attention mechanism may fill the image efficiently by focusing on the contour information. The attention-based gated convolution can effectively capture the global features and compensate for the limitations of the restricted receptive field of the naive convolution. Compared to other models, AGG generates images with finer outline features and no common problems such as the watermark and blur, which shows the best overall performance on the Paris StreetView, CelebA-HQ, and Places2 datasets. The best FID, LPIPS, PSNR, and SSIM values achieved by AGG are 2.18, 0.046, 30.82, and 0.951 on the CelebA-HQ dataset, with at least 3.21% and 6.52% performance improvement on FID and LPIPS compared to state-of-the-art methods, respectively. The source code will be available at https://***/Shawn-Yu-1/AGGNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Convolution

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Slender loris optimization algorithm for solving optimization problems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8799-8810页

作者： Lakshmi, A. Vijaya Vaitheki, K. Joseph, K. Suresh Department of Computer Science School of Engineering and Technology Pondicherry University India

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. © 2024 – IOS Press. All rights reserved.

关键词： Benchmarking

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A BERT-encoded ensembled CNN model for suicide risk identification in social media posts

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Neural Computing and Applications 2024年第18期36卷 10955-10970页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identify suicide risk using a combination of bidirectional encoder representations from transformers (BERT) and an ensemble of multiple convolutional neural networks (CNN). BERT is used to encode the text data into numerical representations that capture the context-aware meaning of words and phrases, while ensemble CNN is used to analyze the encoded text data to identify patterns and relationships relevant to suicide risk. The model is trained on a large corpus of text data from social media and suicide notes and evaluated on a validation set of labeled data. The results of the proposed model perform better than the recent approaches in detecting suicide risk and can potentially be a valuable tool for suicide prevention efforts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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A systematic review on lightweight security algorithms for a sustainable IoT infrastructure

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Sarker, Kamal Uddin Department of Computer Science and MIS Oman College of Management and Technology Barka Oman

Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power for sensing, data extraction, processing, and sharing over the internet. Moreover, IoT devices are in operation with a lack of standards that poses a severe security threat. Intrusion detection systems and cryptographic algorithms are two important components in the security posture of a cyberspace. IoT applications provide real-time services, so security measures are active 24/7/365. Hence, these algorithms are also related to energy consumption. It is a study of security algorithms based on their hardware and software implementations. This review systematically searches for and selects the latest research documents, conducting an analysis of sustainability and security. It depicts the lightweight features of cryptographic algorithms;the relationship of gate density, chip area and power consumption in CMOS technology;and the importance of Machine Learning (MA) applications for IDS and cryptographic solutions in Next Generation IoT (NGIoT). © The Author(s) 2025.

关键词： Intrusion detection

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DRL-Based Joint Optimization of Wireless Charging and Computation Offloading for Multi-Access Edge Computing

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IEEE Transactions on Services Computing 2025年第03期18卷 1352-1367页

作者： Zhu, Xinyuan Hao, Fei Ma, Lianbo Luo, Changqing Min, Geyong Yang, Laurence T. Shaanxi Normal University School of Computer Science Xi'an710119 China Northeastern University School of Software Shenyang100819 China Virginia Commonwealth University Department of Computer Science RichmondVA23084 United States University of Exeter Department of Computer Science United Kingdom Hainan University School of Computer Science and Technology China St. Francis Xavier University Department of Computer Science Canada

Wireless-powered multi-access edge computing (WP-MEC), as a promising computing paradigm with the great potential for breaking through the power limitations of wireless devices, is facing the challenges of reliable task offloading and charging power allocation. Towards this end, we formulate a joint optimization problem of wireless charging and computation offloading in socially-aware D2D-assisted WP-MEC to maximize the utility, characterized by wireless devices' residual energy and the strength of social relationship. To address this problem, we propose a deep reinforcement learning (DRL)-based approach with hybrid actor-critic networks including three actor networks and one critic network as well as with Proximal Policy Optimization (PPO) updating policy. Further, to prevent the policy collapse, we adopt the PPO-clip algorithm which limits the update steps to enhance the stability of algorithm. The experimental results show that the proposed algorithm can achieved superior convergence performance and, meanwhile, improves the average utility efficiently compared to other baseline approaches. © 2008-2012 IEEE.

关键词： Computation offloading

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The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

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computers, Materials & Continua 2024年第4期79卷 329-349页

作者： Qiang Fu JunWang Changfu Si Jiawei Liu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China College of Computer Science and Engineering Northeastern UniversityShenyang110169China

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security *** paper proposes a model for the industrial control *** includes a malware containment strategy that integrates intrusion detection,quarantine,and ***,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment *** addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction ***,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is *** otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control *** earlier the immunization of key nodes,the *** the time exceeds the threshold,immunizing key nodes is almost *** analysis provides a better way to contain the malware in the industrial control network.

关键词： Key nodes dynamic model industrial control network simulation

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