@FedCSIS

FedCSIS conference series is organized by IEEE PS C-16 (Computer Society) Chapter and other IEEE units; publication: IEEE DL, indexed: Scopus (all publications)

 Gdynia, Poland
fedcsis.org
Joined September 2014
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20Media
Anyone into precision viniculture? Then check out the paper: mdpi.com/1424-8220/26/21/670… look also into associated WWW site, where the datasets that we managed to locate are listed. If you have "your own" labeled dataset, you can share with others by submitting it there.

Grapes Ready for Picking: Investigating the Readiness of Precision Viticulture Datasets for Scene...

Public datasets of vineyard imagery were collected for detection, segmentation and disease mapping and are now proposed as a source for 3D scene reconstruction. Their documentation rarely states the...

mdpi.com
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Enabling the discovery of #symbolic, human-readable models: “Symbolic vs Black-Box #Explanations: A Model-Driven Approach Using #GrammaticalEvolution” by D. Sepioło, A. Ligęza. ACSIS Vol. 43 p. 381–386; tinyurl.com/muj56rs4
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Maximizing the total availability of vehicles or minimizing the total maintenance cost in rolling stock scheduling problems: “#Metaheuristics for #RollingStock Cyclic Job Scheduling Problems with #Maintenance” by R. Rudek. ACSIS Vol. 43 p. 375–380; tinyurl.com/bde6betp
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Enhancing the trustworthiness and usability of AQ data in real-world applications: “Enhancing Low-Cost Air #QualitySensors with# AI for #SmartGreenRouting” by L. Po, M. Casari, F. Rollo, et al. ACSIS Vol. 43 p. 369–374; tinyurl.com/4ju2c32
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Multi-stage method for colour #imagesegmentation, with focus on #vesselssegmentation in retinal #fundusimages, using a U-Net based architecture: “Hybrid U-Net segmentation of vessels in fundus eye images” by L. Pawlaczyk. ACSIS Vol. 43 p. 363–368; tinyurl.com/yd7smh77N
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Automatically discovering and documenting inter-endpoint #dependencies: “Enhancing #APIdocumentation by inter-endpoint dependency graphs” by P. Papadeas, D. Gerokonstantis, C. Hadjichristofi, V. Vescoukis. ACSIS Vol. 43 p. 357–362; tinyurl.com/mr4dh7f9
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Real-time integration of #smartmobilityservices: “#DigitalTwin Micro-Services Architecture to Support a Scenario-based #Integration of Smart Mobility Services and Simulations” by D.Nicolas, C. Feltus, RA De Jesus Marques. ACSIS Vol. 43 p. 351–356; tinyurl.com/2mvuc7u7
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Drawbacks and advantages of using different threshold levels for the model’s tolerance for #OOD: “Out-Of-Distribution Is Not Magic: The Clash Between #RejectionRate and #ModelSuccess” by I. Meiri, R. Dubin, A. Dvir, C. Hajaj. ACSIS Vol. 43 p. 345–350; tinyurl.com/322f7wmv
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Harnessing the intricate feedback mechanisms observed in #forestecosystems: “Forest-Inspired #ReinforcementLearning Based On Nature Ecosystem #FeedbackMechanisms” by R. Maskeliūnas, R. Damaševičius. ACSIS Vol. 43 p. 339–344; tinyurl.com/8d2turh4
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Promoting transparency and clinical trust in #bloodglucoseforecasting: “Attention-Based Multi-Task Learning and #PPO #ReinforcementLearning for Explainable Blood Glucose Prediction” by S. Maqsood, E. Belousovienė, R. Maskeliūnas. ACSIS Vol. 43 p. 333–338; tinyurl.com/5t3rjapf
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Proposing 4 specialized classifiers for nails and detachments in work of arts: “#AIclassifier of defects in Artworks captured by active #infraredthermography” by P. Malík, M. Orlej, B. Fajčák, M. Rippa. ACSIS Vol. 43 p. 327–332; tinyurl.com/my2fzbds
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Alternative to conventional approaches from neural #architecturesearch area: “Applying Evolutionary Techniques to Enhance #GraphConvolutionalNetworks for #NodeClassification: Case Studies” by M. Krzywda, S. Lukasik, A. H Gandomi. ACSIS Vol. 43 p. 321–326; tinyurl.com/48228m9mEE
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A new replay snapshotting (RS) technique for strategy learning from past matches in real-time strategy game #StarCraft: Brood War: “StarCraft #strategylearning refinement using #replaysnapshotting” by Š. Krištofík, M. Hanková. ACSIS Vol. 43 p. 315–320; tinyurl.com/yvdasy4t
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Preserving the length of cubic Bézier curves under external deformations for hair strand simulation: “Length-Preserving #HairSimulation via Bézier Curves and Artificial #NeuralNetwork” by K. Gürkahraman, AF Yelkuvan, R. Karakis. ACSIS Vol. 43 p. 309–314; tinyurl.com/3tbuspdj
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Parcel #damagedetection, combining #ML with a custom-built mobile inspection #robot: “Automatic Parcel Damage Recognition Module for an Inspection Robot” by W. Goszczyński, S. Wałęga, J. Chmiel, B. Gawęda, K. Grobler-Dębska. ACSIS Vol. 43 p. 303–308; tinyurl.com/3jbtusru
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Creating a unified, semantically enriched representation of heterogeneous #agritech data: “#DataInteroperability Using #SmartDataModels and NGSI-LD for the Norwegian Agrifood Sector” by R. Dautov, S. Tverdal, AS Bondevik, et al. ACSIS Vol. 43 p. 297–302; tinyurl.com/mwucmhuf
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Depth-assisted single-person pose estimation pipelines in three consumer RGB-D cameras: “A Lightweight #Optimization Approach to the Single-Person #PoseEstimation Pipeline in RGB-D Cameras” by V. Abromavičius, R. Maskeliūnas. ACSIS Vol. 43 p. 287–290; tinyurl.com/np3p65dr
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Container tracking, damage detection during crane unloading operations at container terminals: “Real-time #ContainerTracking and #DamageDetection at Seaports Using #DeepLearning” by S. Vasileiadis, S. Aslam, K. Orphanides, et al. ACSIS Vol. 43 p. 265–276; tinyurl.com/3arrsj5j
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A comprehensive empirical evaluation of four classical #LSTM-based NMT models: “A Comparative Study of LSTM Efficiency vs. #Transformer Power for Localized #TimeSeriesForecasting” by T. Sarkar, H. Chu. ACSIS Vol. 43, p. 265–276; tinyurl.com/54dzjva
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Evaluating the quality of #superresolvedimages: “Keypoint-based metric for evaluating image super-resolution #quality” by J. Sadel, T. Tarasiewicz, P. Kowaleczko, M. Ziaja, D. Kostrzewa, P. Benecki, M. Kawulok. ACSIS Vol. 43 p. 255–263; tinyurl.com/y6e4fd26
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