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Editorial

Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022

by
Machine Learning and Knowledge Extraction Editorial Office
MDPI AG, St. Alban-Anlage 66, 4052 Basel, Switzerland
Mach. Learn. Knowl. Extr. 2023, 5(1), 171-172; https://doi.org/10.3390/make5010011
Published: 18 January 2023
High-quality academic publishing is built on rigorous peer review. Machine Learning and Knowledge Extraction (MAKE) was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. Thanks to the efforts of our reviewers in 2022, the median time to first decision was 18 days and the median time to publication was 42 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown Machine Learning and Knowledge Extraction:
Adolphs, JulianLixandroiu, Radu
Alirezaie, MarjanLo, Yu-Chen
Al-Sarem, MohammedLu, Yan
Alzubaidi, LaithLuo, Ling
Baležentis, TomasMahmood, Muhammad Arif
Balicki, JerzyMajeed, Abdul
Barakhnin, VladimirMarcińczuk, Michał
Barragán Medero, FernandoMei, Qichang
Bazan, Jan G.Mendes, Rui
Brandusoiu, IonutMircea, Sasu Lucian
Brezočnik, LucijaMishra, Akshansh
Bugajev, AndrejMoon, Seonghyeon
Cambria, ErikMurugesan, Mohanraj
Canteli, Mario MañanaNabwey, Hossam A.
Chang, Chia-JungNasser, Nidal
Chen, HongtianNeves, Mariana Lara
Chen, JieNguyen, Trung
Chen, JinghuiNkenyereye, Lewis
Chun, JunhoNovillo, Carlos
Cinque, MarcelloOmlin, Christian W.
Ciorap, RaduOveis, Amir Hosein
Comesaña-Campos, AlbertoPadilla-Vivanco, Alfonso
Csajbók, Zoltán ErnőPanangadan, Anand
Csató, LehelPapadimitriou, Theophilos
D’Alessandro, MarcoPester, Andreas
Damjanović, BoškoPinto, Pedro
Danel, RomanPolyzou, Agoritsa
Dashtian, HassanPoy, Guilhem
Dave, RushitQuevedo Pérez, Jose Ramón
Demarchi, Marco StefanoRadac, Mircea Bogdan
Dhungana Sainju, KarlaRadoglou-Grammatikis, Panagiotis
Dorizzi, BernadetteRana, Pratip
Dronyuk, IvannaRana, Toqir
Du, Ke-LinRelva, Inês Carvalho
Dwivedi, Ashutosh DharResta, Marina
Elbattah, MahmoudRiba Ruiz, Jordi-Roger
Elwood, AdamRubio Alonso, Higinio
Fang, Chin YiRudzionis, Vytautas
Farid, FarnazSalas, Rodrigo
Filippov, AlekseySamuel, John
Foresti, RubenSaranti, Anna
Fusco, AdeleSegall, Richard
Gabbar, Hossam A.Seljan, Sanja
Gabriela, MirceaShabbir, Noman
Gaillard, MartinSikirzhytski, Vitali
Garcia, Carlos Alexandre BorgesSilva, Antonio Jose Ramos
Gimbel, SteveSitek, Wojciech
Godoy, DanielaSneiders, Eriks
Grammatikis, Panagiotis RadoglouStolzenburg, Frieder
Guimarães, VâniaStrutz, Tilo
Hajnal, EvaSujit, Sheeba
Hall, Colin MichaelSun, Chuanheng
Hasan, Md JunayedSun, Tien-Lung
Hashima, SheriefSung, Tae-Eung
Hnatiuc, MihaelaSyed, Khajamoinuddin
Hong, Wei-ChiangSzóstak, Mariusz
Honjo, KeitaTama, Bayu Adhi
Howe, BillTamulevičius, Gintautas
Huang, ShaoguangTang, Yuk Ming
Huang, Shieh-KungTchórzewska-Cieślak, Barbara
Ikonomidou, Vasiliki N.Temerinac-Ott, Maja
Ivanis, PredragTijus, Charles
Jayne, ChrisinaTsiakas, Konstantinos
Jhwueng, Dwueng-ChwuanTung, Chun-Wei
Jiang, YiUdriștoiu, Anca Loredana
Kanavos, AndreasVan Den Honert, Robin C.
Kang, TianVidal, Jorge Maestre
Karakasidis, TheodorosWang, Gang
Kohek, ŠtefanWang, Ping
Kopczewska, KatarzynaWang, Zhihua
Koszela, KrzysztofWinkler, Dave
Koukiou, GeorgiaWollstadt, Patricia
Kumar, SatwantYang, Fan
Kwon, Goo-rakYin, Han
Landekić, MatijaYoung, John
Lee, SuanYuan, Cadmus Ann
Leidner, JochenZeni, Nicola
Lewandowicz, ElżbietaZhan, Xianghao
Li, HengZhang, Huaming
Li, XiaobingZhang, Yang
Liang, JingangZhao, Hui
Lin, Guo-Shiang
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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MDPI and ACS Style

Machine Learning and Knowledge Extraction Editorial Office. Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022. Mach. Learn. Knowl. Extr. 2023, 5, 171-172. https://doi.org/10.3390/make5010011

AMA Style

Machine Learning and Knowledge Extraction Editorial Office. Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022. Machine Learning and Knowledge Extraction. 2023; 5(1):171-172. https://doi.org/10.3390/make5010011

Chicago/Turabian Style

Machine Learning and Knowledge Extraction Editorial Office. 2023. "Acknowledgment to the Reviewers of Machine Learning and Knowledge Extraction in 2022" Machine Learning and Knowledge Extraction 5, no. 1: 171-172. https://doi.org/10.3390/make5010011

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