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Articoli di riviste sul tema "Algorithmes ad"

Autore: Grafiati

Pubblicato: 1 febbraio 2023

Ultima modifica: 19 luglio 2025

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1

Merz, Evan. "Implications of Ad Hoc Artificial Intelligence in Music." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 5 (2021): 35–39. http://dx.doi.org/10.1609/aiide.v10i5.12763.

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This paper is an examination of several well-known applications of artificial intelligence in music generation. The algorithms in EMI, GenJam, WolframTones, and Swarm Music are examined in pursuit of ad hoc modifications. Based on these programs, it is clear that ad hoc modifications occur in most algorithmic music programs. We must keep this in mind when generalizing about computational creativity based on these programs. Ad hoc algorithms model a specific task, rather than a general creative algorithm. The musical metacreation discourse could benefit from the skepticism of the procedural con

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Poselentseva, D. Yu, and E. B. Zamyatina. "An experience of routing algorithms and a data transfer algorithms using simulation." Вестник Пермского университета. Математика. Механика. Информатика, no. 4 (47) (2019): 76–85. http://dx.doi.org/10.17072/1993-0550-2018-4-76-85.

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3

Bessa, Maria. "Algorithmic Design." Architectural Design 79, no. 1 (2009): 120–23. http://dx.doi.org/10.1002/ad.831.

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Cromer, Sara J., Victoria Chen, Christopher Han, et al. "Algorithmic identification of atypical diabetes in electronic health record (EHR) systems." PLOS ONE 17, no. 12 (2022): e0278759. http://dx.doi.org/10.1371/journal.pone.0278759.

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Aims Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review. Methods Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. “Typical” T2D cases were removed by excluding individuals with obesity, eviden

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5

Taniguchi, Hirohito, Michiko Inoue, Toshimitsu Masuzawa, and Hideo Fujiwara. "Clustering algorithms in ad hoc networks." Electronics and Communications in Japan (Part II: Electronics) 88, no. 1 (2004): 51–59. http://dx.doi.org/10.1002/ecjb.20143.

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6

Ma, Renkai, and Yubo Kou. ""How advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–25. http://dx.doi.org/10.1145/3479573.

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To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions thro

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7

Seetaram M Nagaraju, J. "Energy Efficient Routing Algorithm for Future Ad-hoc Wireless Networks." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 736–41. http://dx.doi.org/10.21275/sr24205221635.

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8

S Arun Korath, Sangheethaa. "Comparison of Algorithms used in Blockchain-based Mobile Ad Hoc Networks (MANETs)." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 288–92. http://dx.doi.org/10.21275/sr23404135052.

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9

Aydın, Nevin. "Heuristic Clustering Algorithms in Ad hoc Networks." EMAJ: Emerging Markets Journal 3, no. 3 (2014): 77–80. http://dx.doi.org/10.5195/emaj.2014.39.

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The clustering allows dividing the geographical region to be covered into small zones in which each zone can be handled with a powerful node called clusterhead. The clusterheads have direct communication link with each of its members whereas the member nodes of a cluster must go through the clusterhead to communicate with each other. Since choosing clusterheads optimally is an NP-hard problem, existing solutions to this problem are based on heuristic (mostly greedy) approaches. In this paper, we present three well-known heuristic clustering algorithms: the Lowest-ID, the Highest-Degree, and th

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10

Miralles Pechuán, Luis, Claudia Sánchez Gómez, and Lourdes Martínez Villaseñor. "Ad Exchange Optimization Algorithms on Advertising Networks." Research in Computing Science 84, no. 1 (2014): 77–88. http://dx.doi.org/10.13053/rcs-84-1-7.

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11

Wattenhofer, Roger. "Algorithms for ad hoc and sensor networks." Computer Communications 28, no. 13 (2005): 1498–504. http://dx.doi.org/10.1016/j.comcom.2004.12.037.

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12

Peters, Brady. "Computation Works: The Building of Algorithmic Thought." Architectural Design 83, no. 2 (2013): 8–15. http://dx.doi.org/10.1002/ad.1545.

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13

Register, Yim, and Emma S. Spiro. "Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 817–28. http://dx.doi.org/10.1609/icwsm.v16i1.19337.

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Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or "rabbit hole" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine lear

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14

Llabres, Enriqueta, and Eduardo Rico. "Relational Urban Models: Parameters, Values and Tacit Forms of Algorithms." Architectural Design 86, no. 2 (2016): 84–91. http://dx.doi.org/10.1002/ad.2028.

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15

Chatzigiannakis, Ioannis, Sotiris Nikoletseas, and Paul Spirakis. "Distributed communication algorithms for ad hoc mobile networks." Journal of Parallel and Distributed Computing 63, no. 1 (2003): 58–74. http://dx.doi.org/10.1016/s0743-7315(02)00034-5.

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16

J., Amir. "Matlab Simulations of Ad Hoc Sensors Network Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 1 (2015): 288–93. http://dx.doi.org/10.17762/ijritcc2321-8169.150159.

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17

Pradhan, Nuraj L., and Tarek Saadawi. "Power control algorithms for mobile ad hoc networks." Journal of Advanced Research 2, no. 3 (2011): 199–206. http://dx.doi.org/10.1016/j.jare.2011.04.009.

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18

Pestin, Maxim S., and Alexander S. Novikov. "Simulation model of wireless ad-hoc network to study algorithms of traffic routing." Journal Of Applied Informatics 17, no. 4 (2022): 75–86. http://dx.doi.org/10.37791/2687-0649-2022-17-4-75-86.

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Communication network simulators are software designed to model, explore, test and debug network technologies, including wireless decentralized self-organizing networks or ad-hoc networks. They greatly simplify the research, development and optimization of routing protocols in these networks. However, the well-known simulators have a number of disadvantages, including the difficulty of adding custom extensions to ad-hoc network routing protocols, the lack of the necessary network stack, the lack of routing algorithm visualization modes, low performance, and difficulty in debugging communicatio

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19

Cho, Young-Joo, Yeo-Jin Lee, and Il-Yong Chung. "The establishment of Proactive Routing Selection and Maintenance Algorithms for Mobile Ad Hoc Networks." KIPS Transactions:PartC 14C, no. 1 (2007): 73–80. http://dx.doi.org/10.3745/kipstc.2007.14-c.1.073.

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20

Bernstein, Phil. "Autonomous Algorithmic Architects: Wicked Problems of Machine Learning in Architecture." Architectural Design 94, no. 3 (2024): 118–27. http://dx.doi.org/10.1002/ad.3063.

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AbstractAI is developing at breakneck speed, with generation after generation of software/algorithms swiftly and exponentially appearing in a matter of months of each other. Associate Dean and Professor Adjunct at the Yale School of Architecture Phil Bernstein, an expert in architectural practice and technology, lucidly examines the way the profession has responded to previous digitally enhanced design techniques, and how future uptake of AI technology will affect the workflow, form finding, optimisation and anatomy of the discipline.

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21

A.V., Vlasenko, Köhl A., and Stammer D. "The efficiency of geophysical adjoint codes generated by automatic differentiation tools." Computer Physics Communications 199 (October 30, 2015): 22–28. https://doi.org/10.1016/j.cpc.2015.10.008.

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The accuracy of numerical models that describe complex physical or chemical processes depends on the choice of model parameters. Estimating an optimal set of parameters by optimization algorithms requires knowledge of the sensitivity of the process of interest to model parameters. Typically the sensitivity computation involves differentiation of the model, which can be performed by applying algorithmic differentiation (AD) tools to the underlying numerical code. However, existing AD tools differ substantially in design, legibility and computational efficiency. In this study we show that, for g

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22

Ralphs, T. K., L. Ládanyi, and M. J. Saltzman. "A Library Hierarchy for Implementing Scalable Parallel Search Algorithms." Journal of Supercomputing 28, no. 2 (2004): 215–34. http://dx.doi.org/10.1023/b:supe.0000020179.55383.ad.

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23

Battineni, Gopi, Mohmmad Amran Hossain, Nalini Chintalapudi, et al. "Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms." Diagnostics 11, no. 11 (2021): 2103. http://dx.doi.org/10.3390/diagnostics11112103.

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Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict indivi

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24

Abdallah, Alaa E., Mohammad Bsoul, Emad E. Abdallah, Ibrahim Al–Oqily, and George Kao. "Cluster-Based Online Routing Protocols for Ad Hoc Network." International Journal of Information Technology and Web Engineering 9, no. 4 (2014): 54–66. http://dx.doi.org/10.4018/ijitwe.2014100105.

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In geographical routing algorithms, mobile nodes rely on geographical position to make routing judgments. Researchers frequently discuss such routing algorithms in (2D) space. However, in reality, mobile nodes spread in (3D) space. In this paper the authors present four new 3D geographical-based routing algorithms Cylinder, Greedy-Cylinder, Cluster-Cylinder, and Greedy-cluster-Cylinder. In Cylinder routing, the nodes are locally projected on the inner surface of a cylinder, perimeter routing is executed after that. Greedy-Cylinder starts with Greedy routing algorithm until a local minimum is r

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25

Manaseer, Saher, and Fatima Badwan. "History-Based Backoff Algorithms for Mobile Ad Hoc Networks." Journal of Computer and Communications 04, no. 11 (2016): 37–46. http://dx.doi.org/10.4236/jcc.2016.411004.

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26

Sharma, Sarvesh Kumar. "An Approach In Optimization Of Ad-Hoc Routing Algorithms." International Journal of Distributed and Parallel systems 3, no. 3 (2012): 101–10. http://dx.doi.org/10.5121/ijdps.2012.3310.

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27

Bacsárdi, László, and Sándor Imre. "Information dissemination algorithms in space deployed ad hoc networks." Periodica Polytechnica Electrical Engineering 55, no. 1-2 (2011): 13. http://dx.doi.org/10.3311/pp.ee.2011-1-2.02.

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28

Benchaïba, M., A. Bouabdallah, N. Badache, and M. Ahmed-Nacer. "Distributed mutual exclusion algorithms in mobile ad hoc networks." ACM SIGOPS Operating Systems Review 38, no. 1 (2004): 74–89. http://dx.doi.org/10.1145/974104.974111.

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29

Frey, H. "Scalable geographic routing algorithms for wireless ad hoc networks." IEEE Network 18, no. 4 (2004): 18–22. http://dx.doi.org/10.1109/mnet.2004.1316756.

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30

Iltis, R. A., Seung-Jun Kim, and D. A. Hoang. "Noncooperative iterative MMSE beamforming algorithms for ad hoc networks." IEEE Transactions on Communications 54, no. 4 (2006): 748–59. http://dx.doi.org/10.1109/tcomm.2006.873095.

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31

Ruiz, Patricia, and Pascal Bouvry. "Survey on Broadcast Algorithms for Mobile Ad Hoc Networks." ACM Computing Surveys 48, no. 1 (2015): 1–35. http://dx.doi.org/10.1145/2786005.

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32

Brooks, R. R., and C. Griffin. "Traffic Model Evaluation of Ad Hoc Target Tracking Algorithms." International Journal of High Performance Computing Applications 16, no. 3 (2002): 221–34. http://dx.doi.org/10.1177/10943420020160030301.

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33

Bertossi, Alan A., Stephan Olariu, and Cristina M. Pinotti. "Special issue: Algorithms for wireless and ad-hoc networks." Journal of Parallel and Distributed Computing 66, no. 4 (2006): 487–88. http://dx.doi.org/10.1016/j.jpdc.2006.02.001.

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34

Sharifi, Seyed Ali, and Seyed Morteza Babamir. "Evaluation of Clustering Algorithms in Ad Hoc Mobile Networks." Wireless Personal Communications 109, no. 4 (2019): 2147–86. http://dx.doi.org/10.1007/s11277-019-06673-8.

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35

Mukhtaruzzaman, Mohammad, and Mohammed Atiquzzaman. "Clustering in vehicular ad hoc network: Algorithms and challenges." Computers & Electrical Engineering 88 (December 2020): 106851. http://dx.doi.org/10.1016/j.compeleceng.2020.106851.

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36

Touzene, Abderezak. "Cell-Based Broadcasting Algorithms in Mobile Ad-Hoc Networks." International Journal on Applications of Graph Theory In wireless Ad Hoc Networks And sensor Networks 3, no. 4 (2011): 1–13. http://dx.doi.org/10.5121/jgraphoc.2011.3401.

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37

R. N., Channakeshava. "Security Issues in Mobile Ad-Hoc Networks Routing Algorithms." International Journal Of Latest Technology In Engineering & Management 8, no. 5 (2023): 52–55. http://dx.doi.org/10.56581/ijltem.8.5.52-55.

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38

Sinha, Koushik, and Pradip K. Srimani. "Deterministic Broadcast and Gossiping Algorithms for Ad hoc Networks." Journal of Supercomputing 37, no. 2 (2006): 115–44. http://dx.doi.org/10.1007/s11227-006-6255-3.

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39

A.A. Adas, A. A. Adas. "Simulation of Cryptographic Algorithms in IPSec on Ad-Hoc Networks." journal of King Abdulaziz University Engineering Sciences 28, no. 1 (2017): 31–46. http://dx.doi.org/10.4197/eng.28-1.3.

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This paper focuses on secure data communication between nodes in Ad-Hoc networks by employing IPSec (Internet Protocol Security). In wireless communication, Ad-Hoc network is a new paradigm since, which is used for highly sensitive and emergency operations. Ad-Hoc network is considered a number of mobile nodes that are connected through wireless interfaces and moves arbitrarily. Ensuring security is one of the main issues due to its infrastructure less solutions. This research aims for IPSec protocol that provides security for an Ad-Hoc networking in a various applications. IPSec incorporates

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40

Llach, Daniel Cardoso. "Algorithmic Tectonics: How Cold War Era Research Shaped Our Imagination of Design." Architectural Design 83, no. 2 (2013): 16–21. http://dx.doi.org/10.1002/ad.1546.

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41

Seo, Hoon, Lodewijk Brand, Hua Wang, and Feiping Nie. "Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 522–30. http://dx.doi.org/10.1609/aaai.v35i1.16130.

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Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed t

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42

Chen, Xiao Yun, Sha Liu, and Long Jie Li. "ABCR-STC: An Improved Adaptive Backoff Algorithm for MAC Protocol in Ad Hoc Networks." Applied Mechanics and Materials 40-41 (November 2010): 462–68. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.462.

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The performance of backoff algorithm plays an important role in designing efficient Medium Access Protocols for Ad hoc networks. In this paper, we propose three backoff algorithms for Ad hoc networks. The first algorithm aims at higher fairness, and the second aims at stronger adaptivity. Then, we combine the first two algorithms to propose an optimized backoff algorithm. We use NS2 to simulate Ad hoc network environment to evaluate the performance of our improved backoff algorithms. The results show that our algorithms exhibit a signiﬁcant improvement in throughput and fairness with the exist

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43

Vélez, Jorge I., Luiggi A. Samper, Mauricio Arcos-Holzinger, et al. "A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease." Diagnostics 11, no. 5 (2021): 887. http://dx.doi.org/10.3390/diagnostics11050887.

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Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predictin

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44

Khan, Zulai, and Rashmi Raj. "Comparative Analysis of Green Algorithm within Active Queue Management for Mobile Ad Hoc Network." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 935–38. http://dx.doi.org/10.31142/ijtsrd23950.

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Cui Sidong, 崔思栋, 张磊 Zhang Lei, 张海峰 Zhang Haifeng та ін. "基于十字支撑臂的自适应权重AD-Census抗噪立体匹配算法". Laser & Optoelectronics Progress 62, № 12 (2025): 1215013. https://doi.org/10.3788/lop242334.

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46

G.P., Suja, and Raajan P. "EARLY DIAGNOSIS OF ALZHEIMER’S DISEASE WITH GENERATIVE ADVERSARIAL NETWORKS." ICTACT Journal on Image and Video Processing 14, no. 3 (2024): 3187–93. http://dx.doi.org/10.21917/ijivp.2024.0454.

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Alzheimer’s disease (AD) early diagnosis plays a pivotal role in effective intervention and patient care. With limited and noisy medical imaging datasets, GANs are utilized to generate synthetic brain images, aiding in the augmentation of existing data. The selected classification algorithms are well-established in computer vision and have demonstrated efficacy in image classification tasks. The GAN is employed for data augmentation, creating synthetic images representative of AD-associated features. Subsequently, the augmented dataset is utilized to train and evaluate the performance of multi

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Lukovszki, Tamás, Christian Schindelhauer, and Klaus Volbert. "Resource Efficient Maintenance of Wireless Network Topologies." JUCS - Journal of Universal Computer Science 12, no. (9) (2006): 1292–311. https://doi.org/10.3217/jucs-012-09-1292.

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Multiple hop routing in mobile ad hoc networks can minimize energy consumption and increase data throughput. Yet, the problem of radio interferences remain. However if the routes are restricted to a basic network based on local neighborhoods, these interferences can be reduced such that standard routing algorithms can be applied. We compare different network topologies for these basic networks, i.e. the Yao-graph (aka. Θ-graph) and some also known related models, which will be called the SymmY-graph (aka. YS-graph), the SparsY-graph (aka. YY-graph) and the BoundY-graph. Further, we present a p

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48

Annadurai, C. "Review of Packet Scheduling Algorithms in Mobile Ad Hoc Networks." International Journal of Computer Applications 15, no. 1 (2011): 7–10. http://dx.doi.org/10.5120/1914-2552.

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Sun, Yang, and Ma. "Minimum Connected Dominating Set Algorithms for Ad Hoc Sensor Networks." Sensors 19, no. 8 (2019): 1919. http://dx.doi.org/10.3390/s19081919.

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To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which

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Sherin, B. Cynthia, and E. A. Mary Anita. "A Survey of Scheduling Algorithms for Wireless Ad-hoc Networks." International Journal of Advanced Science and Engineering 4, no. 4 (2018): 776. http://dx.doi.org/10.29294/ijase.4.4.2018.776-787.

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