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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems Science & engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Research of Image De-noising Based on Deep Learning 6

Research of Image De-noising Based on Deep Learning

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Li, Kai Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an710021 China

ISBN: (纸本)9798350305388

Image denoising is a technology to restore the image by changing the information before and after the image sequence. The restored image will become very clear, and it is used a lot now. Due to the imperfect equipment, when the image is transmitted, it will be affected by noise, thus losing the original details. In order to solve this problem and make the noise image clear and obvious, this paper proposes an image denoising method based on deep learning, and constructs the image denoising network structure based on convolutional neural network. At the same time, the directional propagation algorithm is optimized to speed up the training speed of the model and effectively improve the denoising effect. By comparing the experimental data of the classical algorithm experiment, it is concluded that this study has achieved a better denoising effect. Finally, the key issues of image denoising research are summarized, and the development trend of future research in this field is discussed. © 2023 IEEE.

关键词： Image denoising

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Sampling locally, hypothesis globally: accurate 3D point cloud registration with a RANSAC variant

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Visual Intelligence 2023年第1期1卷 142-156页

作者： Yuxin Cheng Zhiqiang Huang SiwenQuan Xinyue Cao Shikun Zhang Jiaqi Yang School of Computer Science Northwestern Polytechnical UniversityXi’an710129China Da-Jiang Innovations ShenZhen511464China School of Electronic and Control Engineering Chang’An UniversityXi’an710064China

Correspondence-based six-degree-of-freedom(6-DoF)pose estimation remains a mainstream solution for 3D point cloud ***,the heavy outliers pose great challenges to this *** this paper,we propose a random sample consensus(RANSAC)variant based on sampling locally and hypothesis globally(SLHG)for 6-DoF pose estimation and 3D point cloud *** key novelties are efficient sampling by guiding the sampling process locally and accurate pose estimation by generating hypotheses with global *** generates a correspondence subset via compatibility clustering on the initial ***,locally guided graph sampling is ***,6-DoF hypotheses are generated by incorporating global information with a voting *** best hypothesis serves as the estimation result by repeating the second and third *** experiments on four popular datasets and comparisons with state-of-the-art methods confirm that:SLHG manages to 1)achieve accurate registrations with a few iterations,and 2)yield better accuracy performance than most competitors.

关键词： 3D point cloud 3D registration Random sample consensus Pose estimation

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Advancing prognostic precision in gastric cancer with an immunoinflammatory index

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World Journal of Gastroenterology 2024年第44期30卷 4754-4758页

作者： Hong-Mei Zhang Guo-Hua Wang Shan-Wen Sun Lei Yuan College of Life Science Northeast Forestry UniversityHarbin 150040Heilongjiang ProvinceChina College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040Heilongjiang ProvinceChina Department of Hepatobiliary Surgery Quzhou People’s HospitalQuzhou 324000Zhejiang ProvinceChina

Gastric cancer remains a major global health challenge with high morbidity and mortality *** advancements in immunology and inflammation research have highlighted the crucial roles that these biological processes play in tumor progression and patient *** has sparked new interest in developing prognostic biomarkers that integrate these two key biological *** this letter,we discuss the recent study by Ba et al,which proposed a novel prognostic immunoinflammatory index for patients with gastric *** underscore the importance of this research,its potential impact on medical practice,and the prospective avenues for further investigation in this rapidly emerging area of study.

关键词： Gastric cancer Immunoinflammatory index Prognostic biomarkers Inflammation Immunology

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On the Design of a Modular Wearable Solution for Assistive Technologies 10

On the Design of a Modular Wearable Solution for Assistive T...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Botezatu, Nicolae Alexandru Lupu, Robert Gabriel Caraiman, Simona Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University of Iaşi Iaşi Romania

ISBN: (纸本)9781665485579

This paper introduces the VibroWear architecture, a solution with a modular design at both hardware and software levels, that integrates an energy-aware engine in order to increase operational autonomy. By providing means of defining policies regarding energy consumption at module or I/O function level, the solution aims at adjusting the operational state of the modules (i.e. full, partial or decreased I/O activity, adjustable sample rates). © 2022 IEEE.

关键词： Wearable technology

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers 49

A Formation Control for Multi-Agent Systems for Event-Trigge...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Asada, Daiki Nakagawa, Takuya Hoshi, Yoshikatsu Oya, Hidetoshi Nagai, Shunya Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan

ISBN: (纸本)9798350331820

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs). © 2023 IEEE.

关键词： Linear matrix inequalities

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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PartDensE: Knowledge Graph Embedding with Joint Use of Specific Learning and Relation-aware Functions 6

PartDensE: Knowledge Graph Embedding with Joint Use of Speci...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Dan, Qiuyue Cui, Delong Peng, Zhiping Li, Qirui He, Jieguang Qiu, JinBo Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Guangdong University of Petrochemical Technology School of Electronic and Information Engineering Maoming China Jiangmen Polytechnic Jiangmen China Guangdong University of Petrochemical Technology School of Computer Maoming China

ISBN: (纸本)9798350386974

Knowledge Graph Embedding (KGE) is important to the value of the Knowledge Graph (KG) in its application field. Currently, neural network-based models achieve the most advanced performance. However, these models rarely consider the potential information between entities and relations in KGs, and also suffer from the problems of complex structure and high consumption of computing resources. In this paper, we propose the simple and efficient model PartDensE by using dense network, combining specific sharing layer and relational awareness layer. Specifically, our model learns all entities and relationships individually and can mine hidden features to enrich global information. Also, learn all the information based on each relation and be able to accurately establish associations between entities and relations. PartDensE can capture global and local features and displayed and hide information, retaining as much valuable information as possible. We evaluated PartDensE with 4 indicators across on two baseline datasets. Numerous experimental data prove the effectiveness of our model. © 2024 IEEE.

关键词： Knowledge graph

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