@article{SaadatfarKhosraviHassannatajJoloudarietal.,
  author    = {Saadatfar, Hamid and Khosravi, Samiyeh and Hassannataj Joloudari, Javad and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {volume 8, issue 2, article 286},
  publisher = {MDPI},
  doi       = {10.3390/math8020286},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40996},
  pages     = {12},
  abstract  = {The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{AmirinasabShamshirbandChronopoulosetal.,
  author    = {Amirinasab, Mehdi and Shamshirband, Shahaboddin and Chronopoulos, Anthony Theodore and Mosavi, Amir and Nabipour, Narjes},
  title     = {Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things},
  series = {electronics},
  volume    = {2020},
  journal   = {electronics},
  number    = {volume 9, issue 2, 320},
  publisher = {MDPI},
  doi       = {10.3390/electronics9020320},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200213-40954},
  pages     = {20},
  abstract  = {The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low-power radio duty-cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the Radio Duty-Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8\% energy consumption in nodes while maintaining up to 99\% of the packet delivery rate (PDR).},
  subject      = {Internet der Dinge},
  language  = {en}
}
@article{ShamshirbandBabanezhadMosavietal.,
  author    = {Shamshirband, Shahaboddin and Babanezhad, Meisam and Mosavi, Amir and Nabipour, Narjes and Hajnal, Eva and Nadai, Laszlo and Chau, Kwok-Wing},
  title     = {Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants},
  series = {Engineering Applications of Computational Fluid Mechanics},
  volume    = {2020},
  journal   = {Engineering Applications of Computational Fluid Mechanics},
  number    = {volume 14, issue 1},
  publisher = {Taylor \& Francis},
  doi       = {10.1080/19942060.2020.1715842},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200227-41013},
  pages     = {367 -- 378},
  abstract  = {A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{StaubachMachacekSkowroneketal.2020,
  author    = {Staubach, Patrick and Machacek, Jan and Skowronek, Josefine and Wichtmann, Torsten},
  title     = {Vibratory pile driving in water-saturated sand: Back-analysis of model tests using a hydro-mechanically coupled CEL method},
  series = {Soils and Foundations},
  volume    = {2021},
  journal   = {Soils and Foundations},
  number    = {Volume 61, Issue 1},
  publisher = {Elsevier, Science Direct},
  address   = {Amsterdam},
  doi       = {10.1016/j.sandf.2020.11.005},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210203-43571},
  pages     = {144 -- 159},
  year      = {2020},
  abstract  = {The development of a hydro-mechanically coupled Coupled-Eulerian-Lagrangian (CEL) method and its application to the back-analysisof vibratory pile driving model tests in water-saturated sand is presented. The predicted pile penetration using this approachis in good agreement with the results of the model tests as well as with fully Lagrangian simulations. In terms of pore water pressure, however, the results of the CEL simulation show a slightly worse accordance with the model tests compared to the Lagrangian simulation. Some shortcomings of the hydro-mechanically coupled CEL method in case of frictional contact problems and pore fluids with high bulk modulus are discussed. Lastly, the CEL method is applied to the simulation of vibratory driving of open-profile piles under partially drained conditions to study installation-induced changes in the soil state. It is concluded that the proposed method is capable of realistically reproducing the most important mechanisms in the soil during the driving process despite its addressed shortcomings.},
  subject      = {Plastische Deformation},
  language  = {en}
}