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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3703-3713页

作者： Zhang, Shengdong Zhang, Xiaoqin Wan, Shaohua Ren, Wenqi Zhao, Liping Zhao, Li Shen, Linlin Shaoxing University Department of Computer Science and Engineering Shaoxing312000 China Shenzhen United Aircraft Technology Company Ltd. Guangdong Shenzhen518129 China Wenzhou University College of Computer and Artificial Intelligence Wenzhou325035 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518110 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Convolutional neural network (CNN)-based dehazing methods have achieved great success in single image dehazing. However, the absence of real-world haze image datasets hinders the deep development of single image dehazing. To address this issue, we propose a diverse hazy image synthesis method based on generative adversarial network (GAN) and matting. Specially, we train a GAN-based model that can transform a gray image into a hazy image. To boost the diversity of hazy images, we propose to simulate hazy images via image matting, which can fuse a real haze image with another image containing diverse objects. To evaluate the performance of dehazing methods, we propose two novel metrics: part-based peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). Extensive experiments are conducted to show the effectiveness of the proposed model, dataset, and criteria. © 2020 IEEE.

关键词： Three dimensional displays

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Reinforcement Learning for Testing Android Applications: A Review 2

Reinforcement Learning for Testing Android Applications: A R...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Usman, Asmau Boukar, Moussa Mahamat Suleiman, Muhammed Aliyu Salihu, Ibrahim Anka Eke, Ndukwe-Oke Abdu Gusau Polytechnic Department of Computer Science Talata Mafara Nigeria Nile University of Nigeria Department of Computer Science Abuja Nigeria Nile University of Nigeria Department of Software Engineering Abuja Nigeria

ISBN: (纸本)9798350358834

This paper offers a review of current research studies that use reinforcement learning (RL) to test Android applications. The primary purpose of this study is to simplify future research by collecting and investigating the current state of Android app testing approaches using the RL technique. We provide a well-defined criterion comprising of seven key points. The key points are: Addressed problems, reasons for using the RL technique, RL algorithms, supported events, testing techniques, validation, and evaluation methods. In the literature, we have analyzed various techniques to evaluate their efficiency. This study showed that model-based testing is the most commonly used testing technique. Q-learning is the best algorithm in terms of predictive accuracy. We identified that code coverage is the most widely used evaluation metric and comparison with other tools and techniques is the preferred validation approach. © 2023 IEEE.

关键词： Reinforcement learning

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Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

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computers, Materials & Continua 2022年第3期70卷 5039-5058页

作者： Khuram Nawaz Khayam Zahid Mehmood Hassan Nazeer Chaudhry Muhammad Usman Ashraf Usman Tariq Mohammed Nawaf Altouri Khalid Alsubhi Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electronics Information and BioengineeringPolitecnico di MilanoMilano20122Italy Department of Computer Science University of Management and TechnologySialkotPakistan College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia Department of Computer Science Imam Mohammad Ibn Saud Islamic UniversityRiyadh13318Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah21589Saudi Arabia

Face recognition is a big challenge in the research field with a lot of problems like misalignment,illumination changes,pose variations,occlusion,and *** a single solution to solve all these problems at a time is a challenging *** have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid *** technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching ***,the input image is recognized using a robust kernel representation method using extracted *** qualitative and quantitative analysis of the proposed method is carried on benchmark image *** results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR,ORL,LFW,and FERET face recognition datasets.

关键词： Face recognition local tetra patterns spatial pyramid matching robust kernel representation max-pooling

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Person Re-Identification Based on Instance Segmentation and Pose Estimation 6

Person Re-Identification Based on Instance Segmentation and ...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Zhou, Yao Qin, Yusong Wang, Song Huang, Zengxi Zhang, Dazhi Xihua University School of Computer Science and Software Engineering Chengdu China Xihua University School of Computer and Software Engineering and the Sichuan Xihua Jiaotong Forensics Center Chengdu China Sichuan Police College Criminal Science and Technology Department Luzhou China

ISBN: (纸本)9798350325485

Person Re-Identification (ReID) is a crucial task in computer vision that aims to match persons captured from non-overlapping camera views. In this paper, to alleviate the impacts of background and occlusion, we propose to use instance segmentation and pose estimation methods to create masks for global feature extraction. Furthermore, we divide the pedestrian images into three regions according to pedestrian keypoints, trying to eliminate the alignment errors. This part-based matching strategy also helps to address the occlusion issue. Overall, we construct a deep learning network with three branches, including two global branches and a part-based branch. The two global branches extract global features using segmentation-based mask and the mask derived from pedestrian keypoint heatmaps, respectively. In the end, a weighted fusion strategy is used to combine the global scores and part-based scores for final classification. This network enables us to acquire robust global feature of pedestrians by excluding background and occlusion, and simultaneously address the alignment errors to some extent. Experimental results on these three widely used datasets demonstrate the effectiveness of our method: Specifically, it achieves 64.5% rank-1 accuracy and 54.3% mAP on Occluded-Duke, and 94.4% and 87.1% rank-1 accuracies on Market-1501 and DukeMTMC-reID, respectively. © 2023 IEEE.

关键词： Deep learning

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A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing 1st

A Novel Machine Learning Approach to Delay Efficient Offload...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Bharti, Piyush Chaudhary, Srishti Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra India Software Development Microland Limited Bengaluru India

ISBN: (纸本)9789811919053

There have been studies on the problems experienced in offloading. But, how to leverage machine learning for resource-efficient computation offloading is not completely explored yet. We present this paper with the motivation to leverage machine learning and backward induction to suggest a method for the resource-efficient computation offloading in IoT devices. In particular, we use machine learning to predict the most efficient device to which a task should be offloaded through the use of a two-model machine learning system deployed in the edge cloud itself, since most high power IoT devices are fixed in their positions and display a certain pattern in their CPU utilization data. This is followed by using backward induction to determine the optimal execution location for each task component. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computation offloading

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Money Laundering Detection and Mitigation Using Decision Tree Ensembles

Money Laundering Detection and Mitigation Using Decision Tre...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Ebenezer, Adeoti Babajide Olutayo, Boyinbode Olumide O, Obe Akintola, K.G. Emmanuel, Mgbeahuruike Oluwayemisi Boye, Fatade Babcock University Software Engineering Department Ogun state Ilisan Remo Nigeria Federal University of Technology Akure Department of Computer Science Ondo state Akure Nigeria

ISBN: (纸本)9798350376838

Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously done on money laundering detection, it was observed that it takes times before fraudulent transactions can be detected, it is also less effective, meaning that is can easily be circumvents and the data sampling methods used in previous researches makes the model to suffer from overfitting and loss of information, however this research adopts SMOTE (Synthetic Minority Oversampling Techniques) algorithm method to balance the dataset so as to have a fair representation of the two classes in order for machine learning model to accurately evaluate the models. The Objective of this research is to adapt and implement decision tree ensemble learning algorithms to detect money laundering activities, also design and implement an Anti Money Laundering (AML) query system to mitigate money laundering activities based on detection results and finally to carry out the performance evaluation of decision tree ensemble algorithms using standard performance metrics. © 2024 IEEE.

关键词： Laundering

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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A Review on Feature Selection Techniques for Anxiety Classification from Physiological Signals 1

A Review on Feature Selection Techniques for Anxiety Classif...

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

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798331518578

Anxiety disorders significantly impact individuals' quality of life and are traditionally assessed using subjective methods, which often lack accuracy. Recent advancements have enabled the use of physiological signals for anxiety classification, providing a more objective approach. However, the multidomain nature of these signals results in high-dimensional feature sets, necessitating effective feature selection. Therefore, this paper reviews various feature selection techniques-filter, wrapper, embedded, and hybrid-used in physiological-based anxiety classification, analyzing the advantages and limitations. It also highlights key challenges and suggests future research directions. Furthermore, this review introduces a proposed study utilizing a novel Corr-FGA hybrid feature selection algorithm to develop an efficient anxiety classification framework. The proposed framework achieves the highest accuracy of 88.93% using 8 features. This review aims to guide researchers in developing a robust, efficient anxiety classification model while providing insights that can help streamline feature selection and optimize machine learning model performance in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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