Relevant bibliographies by topics / HYBRID CLUSTERING

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  1. Journal articles
  2. Dissertations / Theses
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Academic literature on the topic 'HYBRID CLUSTERING'

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

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Journal articles on the topic "HYBRID CLUSTERING"

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Osei-Bryson, Kweku-Muata, and Tasha R. Inniss. "A hybrid clustering algorithm." Computers & Operations Research 34, no. 11 (2007): 3255–69. http://dx.doi.org/10.1016/j.cor.2005.12.004.

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Swati Gupta. "A Novel Hybrid Model Based on Hierarchical and Density Clustering Approaches." Panamerican Mathematical Journal 35, no. 2s (2024): 329–39. https://doi.org/10.52783/pmj.v35.i2s.2575.

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Prediction is a developing domain that requires further advancement. In practical situations, one frequently encounters unsupervised datasets requiring analysis. Clustering techniques can efficiently resolve this difficulty. The current study designs the hybrid hierarchical density model to enhance clustering efficiency through a combination of hierarchical clustering and DBSCAN. Hierarchical clustering's initial partitioning quality, which lays a strong foundation for DBSCAN's refinement, is a key influencing factor for these enhancements in this model. DBSCAN's ability to adjust to local den
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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Boosting k-means clustering with symbiotic organisms search for automatic clustering problems." PLOS ONE 17, no. 8 (2022): e0272861. http://dx.doi.org/10.1371/journal.pone.0272861.

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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performa
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Tusell-Rey, Claudia C., Yenny Villuendas-Rey, Viridiana Salinas-García, Oscar Camacho-Nieto, and Cornelio Yáñez-Márquez. "Instance Selection for Hybrid and Incomplete Data based on Clustering." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 3 (2025): 405–19. https://doi.org/10.61467/2007.1558.2025.v16i3.845.

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This paper presents the HICCS algorithm, a novel clustering approach that handles mixed and incomplete data. HICCS improves clustering by using compact sets as initial clusters, employing holotypes to measure intergroup dissimilarity, and merging clusters based on similarity in an order-independent manner. Additionally, it incorporates a user-defined similarity function, making it adaptable to various real-world domains. Furthermore, we introduce the IS-HICCS algorithm for instance selection, which reduces the instance set without compromising classifier accuracy, highlighting clustering's pot
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Augusteijn, M. F., and U. J. Steck. "Supervised adaptive clustering: A hybrid neural network clustering algorithm." Neural Computing & Applications 7, no. 1 (1998): 78–89. http://dx.doi.org/10.1007/bf01413712.

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Guo, Limin, Weijia Qin, Zhi Cai, and Xing Su. "Hybrid Clustering Algorithm Based on Improved Density Peak Clustering." Applied Sciences 14, no. 2 (2024): 715. http://dx.doi.org/10.3390/app14020715.

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In the era of big data, unsupervised learning algorithms such as clustering are particularly prominent. In recent years, there have been significant advancements in clustering algorithm research. The Clustering by Density Peaks algorithm is known as Clustering by Fast Search and Find of Density Peaks (density peak clustering). This clustering algorithm, proposed in Science in 2014, automatically finds cluster centers. It is simple, efficient, does not require iterative computation, and is suitable for large-scale and high-dimensional data. However, DPC and most of its refinements have several
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Yu, Zhiwen, Le Li, Yunjun Gao, et al. "Hybrid clustering solution selection strategy." Pattern Recognition 47, no. 10 (2014): 3362–75. http://dx.doi.org/10.1016/j.patcog.2014.04.005.

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Amiri, Saeid, Bertrand S. Clarke, Jennifer L. Clarke, and Hoyt Koepke. "A General Hybrid Clustering Technique." Journal of Computational and Graphical Statistics 28, no. 3 (2019): 540–51. http://dx.doi.org/10.1080/10618600.2018.1546593.

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Javed, Ali, and Byung Suk Lee. "Hybrid semantic clustering of hashtags." Online Social Networks and Media 5 (March 2018): 23–36. http://dx.doi.org/10.1016/j.osnem.2017.10.004.

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Rakhi. "Hybrid Technique on Image Clustering." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 354–58. https://doi.org/10.35940/ijeat.F1483.089620.

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Face recognition using FLD for extracting high dimensional images is introduced in this paper. The main purpose is to work on removing bugs and noise from the images and extract the facial expression applied on face descriptor. FLD is selected for increasing the discrimination information [17]. The main points of this paper give the brief knowledge about the face recognition and face clustering. Its shows how biometric terms help the local and global features for extracting information from database. Finding better solutions to deal with noise in face recognition is a challenging task [18]. We
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Dissertations / Theses on the topic "HYBRID CLUSTERING"

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Keller, Jens. "Clustering biological data using a hybrid approach : Composition of clusterings from different features." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1078.

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<p>Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the
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Tyree, Eric William. "A hybrid methodology for data clustering." Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301057.

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Moore, Garrett Lee. "A Hybrid (Active-Passive) VANET Clustering Technique." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1077.

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Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters support Intelligent Transport Systems (ITS) applications and message routing by establishing a more stable global topology. Clustering increases scalability of the VANET by eliminating broadcast storms caused by packet flooding and facilitate multi-channel operation. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messag
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Gurcan, Fatih. "A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611667/index.pdf.

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Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficie
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Tantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.

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Garbiso, Julian Pedro. "Fair auto-adaptive clustering for hybrid vehicular networks." Thesis, Paris, ENST, 2017. http://www.theses.fr/2017ENST0061/document.

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Dans le cadre du développement des innovations dans les Systèmes de Transport Intelligents, les véhicules connectés devront être capables de télécharger des informations basées sur la position sur et depuis des serveurs distants. Ces véhicules seront équipés avec des différentes technologies d’accès radio, telles que les réseaux cellulaires ou les réseaux véhicule-à-véhicule (V2V) comme IEEE 802.11p. Les réseaux cellulaires, avec une couverture presque omniprésente, fournissent un accès à internet avec garanties de qualité de service. Cependant, l’accès à ces réseaux est payant. Dans cette thè
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Garbiso, Julian Pedro. "Fair auto-adaptive clustering for hybrid vehicular networks." Electronic Thesis or Diss., Paris, ENST, 2017. http://www.theses.fr/2017ENST0061.

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Dans le cadre du développement des innovations dans les Systèmes de Transport Intelligents, les véhicules connectés devront être capables de télécharger des informations basées sur la position sur et depuis des serveurs distants. Ces véhicules seront équipés avec des différentes technologies d’accès radio, telles que les réseaux cellulaires ou les réseaux véhicule-à-véhicule (V2V) comme IEEE 802.11p. Les réseaux cellulaires, avec une couverture presque omniprésente, fournissent un accès à internet avec garanties de qualité de service. Cependant, l’accès à ces réseaux est payant. Dans cette thè
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Javed, Ali. "A Hybrid Approach to Semantic Hashtag Clustering in Social Media." ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/623.

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The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to clustering hashtags based on their semantics, designed in two phases. The first phase is a sense-level metadata-based semantic clustering algorithm that has the ability to differentiate among distinct se
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GARRAFFA, MICHELE. "Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2639115.

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This thesis deals with the design of exact and heuristic algorithms for scheduling and clustering combinatorial optimization problems. All the works are linked by the fact that all the presented methods arebasically hybrid algorithms, that mix techniques used in the world of combinatorial optimization. The algorithms are all efficient in practice, but the one presented in Chapter 4, that has mostly theoretical interest. Chapter 2 presents practical solution algorithms based on an ILP model for an energy scheduling combinatorial problem that arises in a smart building context. Chapter 3 presen
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Masoudi, Pedram. "Application of hybrid uncertainty-clustering approach in pre-processing well-logs." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S023/document.

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La thèse est principalement centrée sur l'étude de la résolution verticale des diagraphies. On outre, l'arithmétique floue est appliquée aux modèles expérimentaux pétrophysiques en vue de transmettre l'incertitude des données d'entrée aux données de sortie, ici la saturation irréductible en eau et la perméabilité. Les diagraphies sont des signaux digitaux dont les données sont des mesures volumétriques. Le mécanisme d'enregistrement de ces données est modélisé par des fonctions d'appartenance floues. On a montré que la Résolution Verticale de la Fonction d'Appartenance (VRmf) est supérieur d'e
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Books on the topic "HYBRID CLUSTERING"

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De, Sourav, Sandip Dey, and Siddhartha Bhattacharyya, eds. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley, 2020. http://dx.doi.org/10.1002/9781119551621.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.

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Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.

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Kaur, Arvind, and Nancy Nancy. Comparative Analysis of Hybrid Clustering Algorithm with K- Means. Independently Published, 2018.

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Book chapters on the topic "HYBRID CLUSTERING"

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Awange, Joseph, Béla Paláncz, and Lajos Völgyesi. "Clustering." In Hybrid Imaging and Visualization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26153-5_3.

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Awange, Joseph, Béla Paláncz, and Lajos Völgyesi. "Clustering." In Hybrid Imaging and Visualization. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72817-4_3.

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Przybyła, Tomasz. "Hybrid Fuzzy Clustering Method." In Advances in Soft Computing. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75175-5_8.

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Kogan, Jacob, Charles Nicholas, and Mike Wiacek. "Hybrid Clustering with Divergences." In Survey of Text Mining II. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-046-9_4.

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Singh, Tribhuvan, Krishn Kumar Mishra, and Ranvijay. "Data Clustering Using Environmental Adaptation Method." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_16.

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Oliveira, A. C. M., and L. A. N. Lorena. "Hybrid Evolutionary Algorithms and Clustering Search." In Hybrid Evolutionary Algorithms. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_4.

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Panwar, Surya Nandan, Saliya Goyal, and Prafulla Bafna. "Analytical Study of Starbucks Using Clustering." In Hybrid Intelligent Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_93.

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Chong, A., T. D. Gedeon, and K. W. Wong. "Histogram-Based Fuzzy Clustering and Its Comparison to Fuzzy C-Means Clustering in One-Dimensional Data." In Hybrid Information Systems. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_19.

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Sarkar, Jnanendra Prasad, Indrajit Saha, Anasua Sarkar, and Ujjwal Maulik. "Improving Modified Differential Evolution for Fuzzy Clustering." In Hybrid Intelligent Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_14.

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Smith, Kate A., Sheldon Chuan, and Peter Putten. "Determining the Validity of Clustering for Data Fusion." In Hybrid Information Systems. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_45.

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Conference papers on the topic "HYBRID CLUSTERING"

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Chandra, B. "Hybrid clustering algorithm." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346251.

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Srinoy, Surat, and Werasak Kurutach. "Combination Artificial Ant Clustering and K-PSO Clustering Approach to Network Security Model." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253601.

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Jiang, Sheng-Yi, and Xia Li. "A Hybrid Clustering Algorithm." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.93.

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Gusmão, Renê, Allan Juan Araújo, and Francisco Carvalho. "A distributed approach to cluster multi-view relational data." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2024. http://dx.doi.org/10.21528/cbic2023-098.

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Clustering of multi-view data has become an important research field. The efficient clustering of multi-view data is a challenging problem. This work aimed to investigate a distributed approach to cluster multi-view relational data. A PSO-based hybrid method was used to generate clustering from all views independently. Five different objective functions were explored to induce diversity to the clusterings since each function looks for different cluster structures. Five different consensus functions were compared to produce the final partition from the ensembles. Three multi-view real-world dat
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Niennattrakul, V., and C. A. Ratanamahatana. "Clustering Multimedia Data Using Time Series." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253514.

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Hwang, Junwon, Dooheon Song, and Changhoon Lee. "Performance Analysis of 2-tier Clustering." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253659.

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Faceli, Katti, Andre De Carvalho, and Marcilio De Souto. "Multi-Objective Clustering Ensemble." In 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06). IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264934.

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Dehshibi, Mohammad Mahdi, Meysam Alavi, and Jamshid Shanbehzadeh. "Kernel-based Persian viseme clustering." In 2013 13th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2013. http://dx.doi.org/10.1109/his.2013.6920468.

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Yoo, Jungsoon Park, Chrisila C. Pettey, and Sung Yoo. "A hybrid conceptual clustering system." In the 1996 ACM 24th annual conference. ACM Press, 1996. http://dx.doi.org/10.1145/228329.228341.

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Oduntan, Olayinka Idowu, and Parimala Thulasiraman. "Hybrid Metaheuristic Algorithm for Clustering." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628863.

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Reports on the topic "HYBRID CLUSTERING"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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