Relevant bibliographies by topics / Hybrid data mining

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Hybrid data mining'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "Hybrid data mining"

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Elankavi, R., R. Kalaiprasath, and R. Udayakumar. "DATA MINING WITH BIG DATA REVOLUTION HYBRID." International Journal on Smart Sensing and Intelligent Systems 10, no. 4 (2017): 560–73. http://dx.doi.org/10.21307/ijssis-2017-270.

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Ambulkar, Bhagyashree, and Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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Lakshmi Devasena, C., and M. Hemalatha. "A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm." International Journal of Computer Applications 25, no. 2 (2011): 1–5. http://dx.doi.org/10.5120/3007-4056.

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Tamer, Uçar, and Karahoca Adem. "Benchmarking data mining approaches for traveler segmentation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 409–15. https://doi.org/10.11591/ijece.v11i1.pp409-415.

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The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering us
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Shadroo, Shabnam, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, Morteza Naserbakht, and Mehdi Hosseinzadeh. "Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems." Acta Informatica Pragensia 10, no. 1 (2021): 85–107. http://dx.doi.org/10.18267/j.aip.148.

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Harrag, Fouzi, and Ali Alshehri. "Applying Data Mining in Surveillance." International Journal of Distributed Systems and Technologies 14, no. 1 (2023): 1–24. http://dx.doi.org/10.4018/ijdst.317930.

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In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of
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Azad, Chandrashekhar. "Data Mining based Hybrid Intrusion Detection System." Indian Journal of Science and Technology 7, no. 6 (2014): 781–89. http://dx.doi.org/10.17485/ijst/2014/v7i6.19.

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Sharma, Monica, and Rajdeep Kaur. "Data Mining in Healthcare using Hybrid Approach." International Journal of Computer Applications 128, no. 4 (2015): 49–53. http://dx.doi.org/10.5120/ijca2015906539.

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Abidi, Balkis, Sadok Ben Yahia, and Charith Perera. "Hybrid microaggregation for privacy preserving data mining." Journal of Ambient Intelligence and Humanized Computing 11, no. 1 (2018): 23–38. http://dx.doi.org/10.1007/s12652-018-1122-7.

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Lee, Zne-Jung, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye, and Zhen Chen. "A hybrid system for imbalanced data mining." Microsystem Technologies 26, no. 9 (2019): 3043–47. http://dx.doi.org/10.1007/s00542-019-04566-1.

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Dissertations / Theses on the topic "Hybrid data mining"

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Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.

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Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptio
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Seetan, Raed. "A Data Mining Approach to Radiation Hybrid Mapping." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27315.

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The task of mapping markers from Radiation Hybrid (RH) mapping experiments is typically viewed as equivalent to the traveling-salesman problem, which has combinatorial complexity. As an additional problem, experiments commonly result in some unreliable markers that reduce the overall map quality. Due to the large numbers of markers in current radiation hybrid populations, the use of the data mining techniques becomes increasingly important for reducing both the computational complexity and the impact of noise of the original data. In this dissertation, a clustering-based approach is proposed f
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Zall, Davood. "Visual Data Mining : An Approach to Hybrid 3D Visualization." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16601.

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By increasing the volume and complexity of datasets, Visual Data Mining (VDM), new visualization techniques evolved and new techniques released. However, some of these techniques performing well and cover all expectations; the others failed to save their positions. The main issue of such techniques is problem dependency.In this study, after a short description about necessity of Visual Data Mining techniques, I will provide a classified review of previous researches. This will result in a deep understanding as well as simple accessibility to previous researches, in a concise manner. This will
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Yang, Pengyi. "Ensemble methods and hybrid algorithms for computational and systems biology." Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/28979.

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Modern molecular biology increasingly relies on the application of high-throughput technologies for studying the function, interaction, and integration of genes, proteins, and a variety of other molecules on a large scale. The application of those high throughput technologies has led to the exponential growth of biological data, making modern molecular biology a data-intensive science. Huge effort has been directed to the development of robust and efficient computational algorithms in order to make sense of these extremely large and complex biological data, giving rise to several interdiscipli
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Theobald, Claire. "Bayesian Deep Learning for Mining and Analyzing Astronomical Data." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0081.

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Dans cette thèse, nous abordons le problème de la confiance que nous pouvons avoir en des systèmes prédictifs de type réseaux profonds selon deux directions de recherche complémentaires. Le premier axe s'intéresse à la capacité d'une IA à estimer de la façon la plus juste possible son degré d'incertitude liée à sa prise de décision. Le second axe quant à lui se concentre sur l'explicabilité de ces systèmes, c'est-à-dire leur capacité à convaincre l'utilisateur humain du bien fondé de ses prédictions. Le problème de l'estimation des incertitudes est traité à l'aide de l'apprentissage profond ba
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Cheng, Xueqi. "Exploring Hybrid Dynamic and Static Techniques for Software Verification." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/26216.

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With the growing importance of software on which human lives increasingly depend, the correctness requirement of the underlying software becomes especially critical. However, the increasing complexities and sizes of modern software systems pose special challenges on the effectiveness as well as efficiency of software verification. Two major obstacles include the quality of test generation in terms of error detection in software testing and the state space explosion problem in software formal verification (model checking). In this dissertation, we investigate several hybrid techniques that exp
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Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks." Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.

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pande, anurag. "ESTIMATION OF HYBRID MODELS FOR REAL-TIME CRASH RISK ASSESSMENT ON FREEWAYS." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3016.

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Relevance of reactive traffic management strategies such as freeway incident detection has been diminishing with advancements in mobile phone usage and video surveillance technology. On the other hand, capacity to collect, store, and analyze traffic data from underground loop detectors has witnessed enormous growth in the recent past. These two facts together provide us with motivation as well as the means to shift the focus of freeway traffic management toward proactive strategies that would involve anticipating incidents such as crashes. The primary element of proactive traffic management st
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Sainani, Varsha. "Hybrid Layered Intrusion Detection System." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_theses/44.

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The increasing number of network security related incidents has made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). Detecting intrusion in a distributed network from outside network segment as well as from inside is a difficult problem. IDSs are expected to analyze a large volume of data while not placing a significant added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel hybrid layered multiagent-based intru
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Zhang, Jiapu. "Derivative-free hybrid methods in global optimization and their applications." Thesis, University of Ballarat, 2005. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/34054.

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In recent years large-scale global optimization (GO) problems have drawn considerable attention. These problems have many applications, in particular in data mining and biochemistry. Numerical methods for GO are often very time consuming and could not be applied for high-dimensional non-convex and / or non-smooth optimization problems. The thesis explores reasons why we need to develop and study new algorithms for solving large-scale GO problems .... The thesis presents several derivative-free hybrid methods for large scale GO problems. These methods do not guarantee the calculation of a globa
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Books on the topic "Hybrid data mining"

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Evgenii, Vityaev, ed. Data mining in finance: Advances in relational and hybrid methods. Kluwer Academic, 2000.

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Bergmeir, Philipp. Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20367-2.

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Sifeng, Liu, and Lin Yi 1959-, eds. Hybrid rough sets and applications in uncertain decision-making. Auerbach Publications, 2010.

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Lee, Geuk. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Emilio, Corchado, Abraham Ajith 1968-, and Pedrycz Witold 1953-, eds. Hybrid artificial intelligence systems: Third international workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008 : proceedings. Springer, 2008.

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Daniel, Howard, Ślęzak Dominik, Hong You Sik, and SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Korea, August 23-25, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Daniel, Howard, Kim Haeng-kon, Kim Tai-hoon, et al., eds. Advances in Hybrid Information Technology: First International Conference, ICHIT 2006, Jeju Island, Korea, November 9-11, 2006, Revised Selected Papers. Springer-Verlag Berlin Heidelberg, 2007.

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Daniel, Howard, Ślęzak Dominik, and SpringerLink (Online service), eds. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Lee, Geuk. Convergence and Hybrid Information Technology: 5th International Conference, ICHIT 2011, Daejeon, Korea, September 22-24, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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Zili, Zhang. Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving. Springer, 2004.

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Book chapters on the topic "Hybrid data mining"

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Dani, Virendra, Priyanka Kokate, Surbhi Kushwah, and Swapnil Waghela. "Privacy Preserving Data Mining Technique to Secure Distributed Client Data." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_52.

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Du, Mingjing, and Shifei Ding. "L-DP: A Hybrid Density Peaks Clustering Method." In Data Mining and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_8.

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Sonawani, Shilpa, and Amrita Mishra. "DHPTID-HYBRID Algorithm: A Hybrid Algorithm for Association Rule Mining." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_14.

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Mucherino, A., and L. Liberti. "A VNS-Based Heuristic for Feature Selection in Data Mining." In Hybrid Metaheuristics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_13.

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Smith-Miles, Kate, Brendan Wreford, Leo Lopes, and Nur Insani. "Predicting Metaheuristic Performance on Graph Coloring Problems Using Data Mining." In Hybrid Metaheuristics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30671-6_16.

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Li, Kan, Wensi Mu, Yong Luan, and Shaohua An. "A Hybrid-Sorting Semantic Matching Method." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6_36.

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Shafiq, Sobia, Wasi Haider Butt, and Usman Qamar. "Attack Type Prediction Using Hybrid Classifier." In Advanced Data Mining and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_38.

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Cecotti, Hubert, and Abdel Belaïd. "Hybrid OCR Combination for Ancient Documents." In Pattern Recognition and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_71.

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Lee, Jae Sik, and Jin Chun Lee. "Customer Churn Prediction by Hybrid Model." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_104.

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Rakotomalala, Ricco, Faouzi Mhamdi, and Mourad Elloumi. "Hybrid Feature Ranking for Proteins Classification." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_72.

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Conference papers on the topic "Hybrid data mining"

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Molhem, Mohammed, Raaid Alubady, Zahraa Abbas Hasan Tayyeh, and Baharudin Osman. "Proposing a Hybrid Data Mining Approach for Network Traffic Analysis." In 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS). IEEE, 2024. https://doi.org/10.1109/netapps63333.2024.10823444.

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Grzymala-Busse, J. W., Z. S. Hippe, T. Mroczek, E. Roj, and B. Skowronski. "Data mining experiments on hop processing data." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.32.

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Tiwari, Anil Kumar, G. Ramakrishna, Lokesh Kumar Sharma, and Sunil Kumar Kashyap. "Neural Network and Genetic Algorithm based Hybrid Data Mining Algorithm (Hybrid Data Mining Algorithm)." In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974485.

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Chung, Sheng-Hao, Wei-Han Chang, and Kawuu W. Lin. "A data mining algorithm for mining region-aware cyclic patterns." In 2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/his.2011.6122195.

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Hambaba, M. L. "Intelligent hybrid system for data mining." In IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr). IEEE, 1996. http://dx.doi.org/10.1109/cifer.1996.501832.

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Suraj, Z., and Delimata. "Data Mining Exploration System for Feature Selection Tasks." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253500.

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Hadzic, F., H. Tan, T. S. Dillon, and E. Chang. "Implications of frequent subtree mining using hybrid support definition." In DATA MINING & INFORMATION ENGINEERING 2007. WIT Press, 2007. http://dx.doi.org/10.2495/data070021.

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Xydas, S., A. S. Hassan, C. E. Marmaras, N. Jenkins, and L. M. Cipcigan. "Electric Vehicle Load Forecasting using Data Mining Methods." In Hybrid and Electric Vehicles Conference 2013 (HEVC 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1914.

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Chen, Chunying, Xiongwei Zhou, and Jianzhong Zhang. "Web Data Mining System Based on Web Services." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.258.

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Putri, Awalia W., and Laksmiwati Hira. "Hybrid transformation in privacy-preserving data mining." In 2016 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2016. http://dx.doi.org/10.1109/icodse.2016.7936114.

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Reports on the topic "Hybrid data mining"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa
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