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Image enhancement with intensity transformation on embedding space

CAAI Transactions on intelligence Technology 2024年第1期9卷 101-115页

作者： Hanul Kim Yeji Jeon Yeong Jun Koh Department of Applied Artificial Intelligence Seoul National University of Science and TechnologySeoulSouth Korea Department of Computer Science and Engineering Chungnam National UniversityDaejeonSouth Korea

In recent times,an image enhancement approach,which learns the global transformation function using deep neural networks,has gained ***,many existing methods based on this approach have a limitation:their transformation functions are too simple to imitate complex colour transformations between low-quality images and manually retouched high-quality *** order to address this limitation,a simple yet effective approach for image enhancement is *** proposed algorithm based on the channel-wise intensity transformation is ***,this transformation is applied to the learnt embedding space instead of specific colour spaces and then return enhanced features to *** this end,the authors define the continuous intensity transformation(CIT)to describe the mapping between input and output intensities on the embedding ***,the enhancement network is developed,which produces multi-scale feature maps from input images,derives the set of transformation functions,and performs the CIT to obtain enhanced *** experiments on the MIT-Adobe 5K dataset demonstrate that the authors’approach improves the performance of conventional intensity transforms on colour space ***,the authors achieved a 3.8%improvement in peak signal-to-noise ratio,a 1.8%improvement in structual similarity index measure,and a 27.5%improvement in learned perceptual image patch ***,the authors’algorithm outperforms state-of-the-art alternatives on three image enhancement datasets:MIT-Adobe 5K,Low-Light,and Google HDRþ.

关键词： computer vision deep learning image enhancement image processing

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Points of interest in the city of Barcelos in Portugal through augmented reality

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 40-48页

作者： Pereira, Miguel Silva, João Carlos Pinheiro, Marisa Carvalho, Sandro Santos, Gilberto Polytechnic Institute of Cávado and Ave Barcelos Portugal 2Ai – Applied Artificial Intelligence Laboratory Barcelos Portugal INESC TEC - Institute for Systems and Computer Engineering Porto Portugal LIACC - Laboratory of Artificial Intelligence and Computer Science Porto Portugal

Barcelos is a historic city in Portugal with many tourist attractions, attracting more and more visitors who come to the city with the aim of exploring it. The main objective of this article is to boost tourism in the city of Barcelos, specifically highlighting tourist, historical and leisure spots, based on the development of a mobile application using augmented reality technologies and geolocation. This application intends to allow the users to know historical points of interest in Barcelos, as well as interact with a certain point. The results of this investigation were evaluated by testing the application by end users, with the aim of identifying whether the application meets their needs, in particular the promotion of tourist and historical points. © 2023

关键词： Augmented reality

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Boosted Spider Wasp Optimizer for High-dimensional Feature Selection

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Journal of Bionic Engineering 2024年第5期21卷 2424-2459页

作者： Elfadil A.Mohamed Malik Sh.Braik Mohammed Azmi Al-Betar Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman University346AjmanUnited Arab Emirates Department of Computer Science Al-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Technology Al-Balqa Applied UniversityIrbid21510Jordan Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman University346AjmanUnited Arab Emirates

With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult *** is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between *** of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search *** is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of *** paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with *** shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of *** concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and *** exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search ***,quantum-based optimization was added to enhance the diversity of the search agents in *** proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy *** was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI *** results were compared with those of traditional BSWO and other well-known meta-heuristics-based *** findings indicate that,in comparison to other competing techn

关键词： High-dimensional features SWO algorithm Feature selection Optimization Machine learning

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Exploration of Diffusion-Based Test Case Generation of Autonomous Driving Systems

Exploration of Diffusion-Based Test Case Generation of Auton...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Joonwoo Uk-Jin Lee, Scott Hanyang University Major in Bio-Artificial Intelligence Department of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Department of Computer Science and Engineering Ansan Korea Republic of

ISBN: (纸本)9798331510756

Autonomous driving systems demand extensive testing to guarantee safety and reliability. However, real-world testing is often costly and limited in variety. This paper investigates the use of diffusion models to generate synthetic driving images as a means of augmenting test datasets for autonomous driving systems. Leveraging their capability to produce diverse and highly realistic images, diffusion models can simulate various driving conditions, including rare or challenging scenarios that are difficult to replicate. By incorporating these synthetic images into the testing pipeline, self-driving systems can be assessed across a wider range of conditions, enhancing their robustness and safety. © 2025 IEEE.

关键词： Autonomous Driving Diffusion Test Generation

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Revisiting 30 years of the Network Time Protocol 24

Revisiting 30 years of the Network Time Protocol

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Woo, Simon S. Computer Science and Engineering Department Applied Data Science Department Department of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400701726

Since the inception of the Internet and WWW, providing the time among multiple nodes on the Internet has been one of the most critical challenges. David Mills is the pioneer to provide time on the Internet, inventing the Network Time Protocol (NTP), and synchronizing the clocks in computer systems. Now, the NTP is predominantly used on the Internet and WWW. In this paper, we revisit the NTP, and present the overview of the NTP. In particular, we highlight the advanced research effort, the SpaceNTP, to synchronize the clocks among assets and entities in space. The SpaceNTP designed for space environments will be the fundamental medium and enabling block to provide the future web services in space. © 2024 Copyright held by the owner/author(s).

关键词： Web services

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

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Learning Dual-Layer User Representation for Enhanced Item Recommendation

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computers, Materials & Continua 2024年第7期80卷 949-971页

作者： Fuxi Zhu Jin Xie Mohammed Alshahrani Applied Research Center of Artificial Intelligence Wuhan CollegeWuhan430212China College of Computer Science South-Central MINZU UniversityWuhan430074China Unmanned.Company Riyadh11564Saudi Arabia

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured ***-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user ***,these technologies only extract common features in historical records and cannot represent changes in user ***,sequential feature can express the user’s interests and intentions that change time by *** the sequential recommendation results based on the user representation of the item lack the interpretability of preference *** address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer ***,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for *** sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s ***,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of *** extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation *** addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

关键词： User representation latent semantic sequential feature interpretability

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Fast K-Medoids With the l1-Norm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第4期5卷 1769-1777页

作者： Capo, Marco Perez, Aritz Lozano, Jose A. Basque Center of Applied Mathematics Bilbao48009 Spain University of the Basque Country UPV/EHU Intelligent Systems Group Department of Computer Science and Artificial Intelligence San Sebastián20018 Spain

K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the obtained solutions strongly depends upon their initialization phase. In this work, we propose an algorithm for the K-medoids problem on the l1-norm/Manhattan distance with a computational complexity of O(n max lbrace log n, Kd), along with theoretical guarantees in terms of the accuracy of the obtained approximation. In addition, we propose a cheap split-merge mechanism that can be used to restart the proposed algorithm after its convergence to a fixed point. Under some mild assumptions, we prove that such a restart procedure reduces the error of the given fixed point. The work also includes an extensive experimentation, in which we compare our method to the most popular approaches for the K-medoids problem: PAM, CLARA, and Park's K-medoids. The obtained empirical results show the proposed algorithm to consistently converge to the solutions with the lowest errors, up to two orders of magnitude of relative error lower than the previously mentioned methods, while also requiring the lowest computational running times among them: up to three orders of magnitude lower. © 2020 IEEE.

关键词： Clustering algorithms

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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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