{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:55Z","timestamp":1740108055430,"version":"3.37.3"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Tubitak","award":["122E315"],"award-info":[{"award-number":["122E315"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-09092-w","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T15:02:32Z","timestamp":1697468552000},"page":"1237-1259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DSHFS: a new hybrid approach that detects structures with their spatial location from large volume satellite images using CNN, GeoServer and TileCache"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5623-8577","authenticated-orcid":false,"given":"Murat","family":"Ta\u015fy\u00fcrek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehmet U\u011fur","family":"T\u00fcrkdamar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Celal","family":"\u00d6zt\u00fcrk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"9092_CR1","unstructured":"Loukides M (2011) What Is Data Science? O\u2019Reilly Media, Inc.,???"},{"key":"9092_CR2","first-page":"1","volume":"14","author":"RL Villars","year":"2011","unstructured":"Villars RL, Olofson CW, Eastwood M (2011) Big data: what it is and why you should care. White Paper, IDC 14:1\u201314","journal-title":"White Paper, IDC"},{"key":"9092_CR3","doi-asserted-by":"publisher","first-page":"6697","DOI":"10.1007\/s00521-020-05449-7","volume":"33","author":"C Corbane","year":"2021","unstructured":"Corbane C, Syrris V, Sabo F, Politis P, Melchiorri M, Pesaresi M, Soille P, Kemper T (2021) Convolutional neural networks for global human settlements mapping from sentinel-2 satellite imagery. Neural Comput Appl 33:6697\u20136720","journal-title":"Neural Comput Appl"},{"key":"9092_CR4","doi-asserted-by":"publisher","first-page":"8529","DOI":"10.1007\/s00521-019-04349-9","volume":"32","author":"V Alhassan","year":"2020","unstructured":"Alhassan V, Henry C, Ramanna S, Storie C (2020) A deep learning framework for land-use\/land-cover mapping and analysis using multispectral satellite imagery. Neural Comput Appl 32:8529\u20138544","journal-title":"Neural Comput Appl"},{"key":"9092_CR5","doi-asserted-by":"publisher","first-page":"2973","DOI":"10.1007\/s00521-020-05151-8","volume":"33","author":"AN Muhammad","year":"2021","unstructured":"Muhammad AN, Aseere AM, Chiroma H, Shah H, Gital AY, Hashem IAT (2021) Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects. Neural Comput Appl 33:2973\u20133009","journal-title":"Neural Comput Appl"},{"issue":"12","key":"9092_CR6","doi-asserted-by":"publisher","first-page":"9511","DOI":"10.1007\/s00521-022-07104-9","volume":"34","author":"A Bouguettaya","year":"2022","unstructured":"Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2022) Deep learning techniques to classify agricultural crops through uav imagery: A review. Neural Comput Appl 34(12):9511\u20139536","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9092_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1115\/1.1451162","volume":"55","author":"O Montenbruck","year":"2002","unstructured":"Montenbruck O, Gill E, Lutze F (2002) Satellite orbits: models, methods, and applications. Appl Mech Rev 55(2):27\u201328","journal-title":"Appl Mech Rev"},{"key":"9092_CR8","doi-asserted-by":"crossref","unstructured":"Ta\u015fy\u00fcrek M, \u00d6zt\u00fcrk C (2022) Ddl: a new deep learning based approach for multiple house numbers detection and clustering. J Facul Eng Arch Gazi Univ 37(2)","DOI":"10.17341\/gazimmfd.908332"},{"issue":"11","key":"9092_CR9","doi-asserted-by":"publisher","first-page":"6533","DOI":"10.1007\/s00521-019-04086-z","volume":"32","author":"Z Liang","year":"2020","unstructured":"Liang Z, Shao J, Zhang D, Gao L (2020) Traffic sign detection and recognition based on pyramidal convolutional networks. Neural Comput Appl 32(11):6533\u20136543","journal-title":"Neural Comput Appl"},{"issue":"15","key":"9092_CR10","doi-asserted-by":"publisher","first-page":"9241","DOI":"10.1007\/s00521-021-05688-2","volume":"33","author":"F Gao","year":"2021","unstructured":"Gao F, Ji S, Guo J, Li Q, Ji Y, Liu Y, Feng S, Wei H, Wang N, Yang B (2021) Id-net: an improved mask r-cnn model for intrusion detection under power grid surveillance. Neural Comput Appl 33(15):9241\u20139257","journal-title":"Neural Comput Appl"},{"issue":"15","key":"9092_CR11","doi-asserted-by":"publisher","first-page":"9289","DOI":"10.1007\/s00521-021-05690-8","volume":"33","author":"B Kim","year":"2021","unstructured":"Kim B, Yuvaraj N, Sri Preethaa K, Arun Pandian R (2021) Surface crack detection using deep learning with shallow cnn architecture for enhanced computation. Neural Comput Appl 33(15):9289\u20139305","journal-title":"Neural Comput Appl"},{"issue":"23","key":"9092_CR12","doi-asserted-by":"publisher","first-page":"7180","DOI":"10.1002\/cpe.7180","volume":"34","author":"RS Arslan","year":"2022","unstructured":"Arslan RS, Tasyurek M (2022) Amd-cnn: android malware detection via feature graph and convolutional neural networks. Concurr Comput Pract Exp 34(23):7180","journal-title":"Concurr Comput Pract Exp"},{"key":"9092_CR13","doi-asserted-by":"crossref","unstructured":"Wang W, Li Y, Zou T, Wang X, You J, Luo Y (2020) A novel image classification approach via dense-mobilenet models. Mobile Information Systems 2020","DOI":"10.1155\/2020\/7602384"},{"key":"9092_CR14","doi-asserted-by":"crossref","unstructured":"Bharati P, Pramanik A (2020) Deep learning techniques-r-cnn to mask r-cnn: a survey. Comput Intell Pattern Recognit 657\u2013668","DOI":"10.1007\/978-981-13-9042-5_56"},{"issue":"13","key":"9092_CR15","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1080\/10298436.2020.1714047","volume":"22","author":"Y Du","year":"2021","unstructured":"Du Y, Pan N, Xu Z, Deng F, Shen Y, Kang H (2021) Pavement distress detection and classification based on yolo network. Int J Pavement Eng 22(13):1659\u20131672","journal-title":"Int J Pavement Eng"},{"key":"9092_CR16","doi-asserted-by":"crossref","unstructured":"Ta\u015fy\u00fcrek M (2023) Odrp: a new approach for spatial street sign detection from exif using deep learning-based object detection, distance estimation, rotation and projection system. Vis Comput 1\u201321","DOI":"10.1007\/s00371-023-02827-9"},{"issue":"4","key":"9092_CR17","doi-asserted-by":"publisher","first-page":"7542","DOI":"10.1002\/cpe.7542","volume":"35","author":"C \u00d6zt\u00fcrk","year":"2023","unstructured":"\u00d6zt\u00fcrk C, Ta\u015fy\u00fcrek M, T\u00fcrkdamar MU (2023) Transfer learning and fine-tuned transfer learning methods\u2019 effectiveness analyse in the cnn-based deep learning models. Concurr Comput Pract Exp 35(4):7542","journal-title":"Concurr Comput Pract Exp"},{"issue":"3","key":"9092_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11554-023-01311-w","volume":"20","author":"M Tasyurek","year":"2023","unstructured":"Tasyurek M, Arslan RS (2023) Rt-droid: a novel approach for real-time android application analysis with transfer learning-based cnn models. J Real-Time Image Proc 20(3):1\u201317","journal-title":"J Real-Time Image Proc"},{"key":"9092_CR19","unstructured":"Agafonkin, V.: Leaflet. https:\/\/leafletjs.com\/ Accessed 2022-08-05"},{"issue":"3","key":"9092_CR20","first-page":"1720","volume":"9","author":"S Dhingra","year":"2019","unstructured":"Dhingra S, Kumar D (2019) A review of remotely sensed satellite image classification. Int J Electr Comput Eng 9(3):1720","journal-title":"Int J Electr Comput Eng"},{"key":"9092_CR21","first-page":"317","volume-title":"Intelligent and cloud computing","author":"M Sahu","year":"2021","unstructured":"Sahu M, Dash R (2021) A survey on deep learning: convolution neural network (cnn). Intelligent and cloud computing. Springer, Berlin, pp 317\u2013325"},{"issue":"5","key":"9092_CR22","doi-asserted-by":"publisher","first-page":"707","DOI":"10.3390\/w14050707","volume":"14","author":"S Gadamsetty","year":"2022","unstructured":"Gadamsetty S, Ch R, Ch A, Iwendi C, Gadekallu TR (2022) Hash-based deep learning approach for remote sensing satellite imagery detection. Water 14(5):707","journal-title":"Water"},{"issue":"2","key":"9092_CR23","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s12524-021-01475-7","volume":"50","author":"A Sharifi","year":"2022","unstructured":"Sharifi A, Mahdipour H, Moradi E, Tariq A (2022) Agricultural field extraction with deep learning algorithm and satellite imagery. J Indian Soc Remote Sens 50(2):417\u2013423","journal-title":"J Indian Soc Remote Sens"},{"key":"9092_CR24","unstructured":"Sirko W, Kashubin S, Ritter M, Annkah A, Bouchareb YSE, Dauphin Y, Keysers D, Neumann M, Cisse M, Quinn J (2021) Continental-scale building detection from high resolution satellite imagery. arXiv preprint arXiv:2107.12283"},{"issue":"11","key":"9092_CR25","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"9092_CR26","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25"},{"issue":"10","key":"9092_CR27","doi-asserted-by":"publisher","first-page":"5353","DOI":"10.1007\/s00521-020-05325-4","volume":"33","author":"NS Chandel","year":"2021","unstructured":"Chandel NS, Chakraborty SK, Rajwade YA, Dubey K, Tiwari MK, Jat D (2021) Identifying crop water stress using deep learning models. Neural Comput Appl 33(10):5353\u20135367","journal-title":"Neural Comput Appl"},{"issue":"17","key":"9092_CR28","doi-asserted-by":"publisher","first-page":"10881","DOI":"10.1007\/s00521-020-05529-8","volume":"33","author":"Z Ma","year":"2021","unstructured":"Ma Z, Mei G, Piccialli F (2021) Machine learning for landslides prevention: a survey. Neural Comput Appl 33(17):10881\u201310907","journal-title":"Neural Comput Appl"},{"key":"9092_CR29","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9092_CR30","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2013) Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv. doi:1048550\/ARXIV.1311.2524","DOI":"10.1109\/CVPR.2014.81"},{"key":"9092_CR31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1506.02640","author":"J Redmon","year":"2015","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2015) You only look once: unified. Real-Time Object Detect. https:\/\/doi.org\/10.48550\/ARXIV.1506.02640","journal-title":"Real-Time Object Detect"},{"key":"9092_CR32","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"issue":"5","key":"9092_CR33","doi-asserted-by":"publisher","first-page":"3895","DOI":"10.1007\/s00521-021-06651-x","volume":"34","author":"AM Roy","year":"2022","unstructured":"Roy AM, Bose R, Bhaduri J (2022) A fast accurate fine-grain object detection model based on yolov4 deep neural network. Neural Comput Appl 34(5):3895\u20133921","journal-title":"Neural Comput Appl"},{"issue":"3","key":"9092_CR34","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s00521-019-04069-0","volume":"32","author":"A Naseer","year":"2020","unstructured":"Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining parkinson\u2019s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839\u2013854","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9092_CR35","doi-asserted-by":"publisher","first-page":"565","DOI":"10.5194\/soil-6-565-2020","volume":"6","author":"W Ng","year":"2020","unstructured":"Ng W, Minasny B, Mendes WdS, Dematt\u00ea JAM (2020) The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data. Soil 6(2):565\u2013578","journal-title":"Soil"},{"key":"9092_CR36","volume-title":"Introductory digital image processing: a remote sensing perspective","author":"RJ John","year":"2016","unstructured":"John RJ (2016) Introductory digital image processing: a remote sensing perspective. Prentice Hall, New Jersey"},{"key":"9092_CR37","doi-asserted-by":"publisher","first-page":"14777","DOI":"10.1007\/s00521-022-07311-4","volume":"34","author":"M Tasyurek","year":"2022","unstructured":"Tasyurek M, Celik M (2022) 4d-gwr: geographically, altitudinal, and temporally weighted regression. Neural Comput Appl 34:14777\u201314791","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9092_CR38","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1080\/01431161.2012.705443","volume":"34","author":"G Cheng","year":"2013","unstructured":"Cheng G, Guo L, Zhao T, Han J, Li H, Fang J (2013) Automatic landslide detection from remote-sensing imagery using a scene classification method based on bovw and plsa. Int J Remote Sens 34(1):45\u201359","journal-title":"Int J Remote Sens"},{"issue":"5","key":"9092_CR39","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1080\/014311697218593","volume":"18","author":"R Richter","year":"1997","unstructured":"Richter R (1997) Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. Int J Remote Sens 18(5):1099\u20131111","journal-title":"Int J Remote Sens"},{"issue":"Part 6","key":"9092_CR40","first-page":"6","volume":"34","author":"M Shand","year":"2002","unstructured":"Shand M (2002) Mapping and imaging Africa on the internet. Int Arch Photogramm Remote Sens Spatial Inf Sci 34(Part 6):6","journal-title":"Int Arch Photogramm Remote Sens Spatial Inf Sci"},{"key":"9092_CR41","doi-asserted-by":"publisher","DOI":"10.4324\/9780203472095","volume-title":"Small-scale map projection design","author":"F Canters","year":"2002","unstructured":"Canters F (2002) Small-scale map projection design. CRC Press, Boca Raton"},{"key":"9092_CR42","doi-asserted-by":"crossref","unstructured":"Nicolai R, Simensen G (2008) The new epsg geodetic parameter registry. In: 70th EAGE conference and exhibition incorporating SPE EUROPEC 2008, p. 40. European Association of Geoscientists and Engineers","DOI":"10.3997\/2214-4609.20147655"},{"key":"9092_CR43","unstructured":"Jain S, Barclay T (2003) Adding the EPSG: 4326 geographic longitude-latitude projection to TerraServer. August"},{"key":"9092_CR44","volume-title":"GeoServer beginner\u2019s guide","author":"B Youngblood","year":"2013","unstructured":"Youngblood B (2013) GeoServer beginner\u2019s guide. Packt Publishing Ltd, Birmingham"},{"key":"9092_CR45","volume-title":"Mastering GeoServer","author":"C Henderson","year":"2014","unstructured":"Henderson C (2014) Mastering GeoServer. Packt Publishing Ltd, Birmingham"},{"key":"9092_CR46","unstructured":"Cepicky J, Gnip P, Kafka S, Koskova I, Charvat K, Nagatsuka T, Ninomiya S (2008) Geospatial data management and integration of geospatial web services. IAALD AFITA WCCA2008, Tokyo"},{"key":"9092_CR47","doi-asserted-by":"crossref","unstructured":"Ta\u015fy\u00fcrek M (2021) Regenerating large volume vector layers with a denormalization-based method. In: 2021 6th international conference on computer science and engineering (UBMK), pp 124\u2013128. IEEE","DOI":"10.1109\/UBMK52708.2021.9558893"},{"key":"9092_CR48","doi-asserted-by":"crossref","unstructured":"Albawi S, Mohammed TA, Al-Zawi S (2017) Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1\u20136. Ieee","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"9092_CR49","doi-asserted-by":"crossref","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"9092_CR50","doi-asserted-by":"publisher","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. https:\/\/doi.org\/10.48550\/ARXIV.1704.04861","DOI":"10.48550\/ARXIV.1704.04861"},{"key":"9092_CR51","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. https:\/\/doi.org\/10.48550\/ARXIV.1801.04381","DOI":"10.48550\/ARXIV.1801.04381"},{"key":"9092_CR52","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen L-C, Chen B, Tan M, Wang W, Zhu Y, Pang R, Vasudevan V, Le Q, Adam H (2019) Searching for mobilenetv3","DOI":"10.1109\/ICCV.2019.00140"},{"key":"9092_CR53","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN https:\/\/doi.org\/10.48550\/ARXIV.1504.08083","DOI":"10.48550\/ARXIV.1504.08083"},{"key":"9092_CR54","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. https:\/\/doi.org\/10.48550\/ARXIV.1506.01497","DOI":"10.48550\/ARXIV.1506.01497"},{"key":"9092_CR55","first-page":"21","volume-title":"Ssd: Single shot multibox detector","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. Springer, Berlin, pp 21\u201337"},{"key":"9092_CR56","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"9092_CR57","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"9092_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114602","volume":"172","author":"Y Liu","year":"2021","unstructured":"Liu Y, Sun P, Wergeles N, Shang Y (2021) A survey and performance evaluation of deep learning methods for small object detection. Expert Syst Appl 172:114602","journal-title":"Expert Syst Appl"},{"key":"9092_CR59","unstructured":"Stackexchange: Calculate lat lon bounds for individual tile generated from Gdal2tiles. https:\/\/gis.stackexchange.com\/questions\/17278\/calculate-lat-lon-bounds-for-individual-tile-generated-from-gdal2tiles Accessed 2022\u201310\u201322"},{"key":"9092_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104507","volume":"26","author":"M Kulawiak","year":"2019","unstructured":"Kulawiak M (2019) Client-side versus server-side geographic data processing performance comparison: data and code. Data Brief 26:104507","journal-title":"Data Brief"},{"key":"9092_CR61","unstructured":"Versloot C (2022) How to create a train\/test split for your machine learning model? https:\/\/github.com\/christianversloot\/machine-learning-articles Accessed 2022\u201306\u201303"},{"key":"9092_CR62","unstructured":"Skalski P (2019) Make sense. https:\/\/github.com\/SkalskiP\/make-sense\/"},{"key":"9092_CR63","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ebiom.2019.04.055","volume":"43","author":"A Dascalu","year":"2019","unstructured":"Dascalu A, David E (2019) Skin cancer detection by deep learning and sound analysis algorithms: a prospective clinical study of an elementary dermoscope. EBioMedicine 43:107\u2013113","journal-title":"EBioMedicine"},{"key":"9092_CR64","doi-asserted-by":"crossref","unstructured":"Hordiiuk D, Oliinyk I, Hnatushenko V, Maksymov K (2019) Semantic segmentation for ships detection from satellite imagery, pp 454\u2013457. IEEE","DOI":"10.1109\/ELNANO.2019.8783822"},{"key":"9092_CR65","doi-asserted-by":"crossref","unstructured":"Muppalaneni NB, Ma M, Gurumoorthy S, Kannan R, Vasanthi V (2019) Machine learning algorithms with roc curve for predicting and diagnosing the heart disease. Soft Comput Med Bioinf 63\u201372","DOI":"10.1007\/978-981-13-0059-2_8"},{"key":"9092_CR66","first-page":"1","volume-title":"Encyclopedia of machine learning and data mining","author":"PA Flach","year":"2016","unstructured":"Flach PA (2016) Roc analysis. Encyclopedia of machine learning and data mining. Springer, Berlin, pp 1\u20138"},{"key":"9092_CR67","doi-asserted-by":"publisher","first-page":"14975","DOI":"10.1007\/s00521-021-06133-0","volume":"33","author":"I Ozer","year":"2021","unstructured":"Ozer I, Cetin O, Gorur K, Temurtas F (2021) Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset. Neural Comput Appl 33:14975\u201314989","journal-title":"Neural Comput Appl"},{"key":"9092_CR68","doi-asserted-by":"crossref","unstructured":"Klein J, Gorton I (2015) Runtime performance challenges in big data systems. In: Proceedings of the 2015 workshop on challenges in performance methods for software development, pp 17\u201322","DOI":"10.1145\/2693561.2693563"},{"issue":"4","key":"9092_CR69","doi-asserted-by":"publisher","first-page":"388","DOI":"10.17694\/bajece.1059070","volume":"10","author":"M Ta\u015fy\u00fcrek","year":"2022","unstructured":"Ta\u015fy\u00fcrek M (2022) A novel approach to improve the performance of the database storing big data with time information. Balkan J Electr Comput Eng 10(4):388\u2013396","journal-title":"Balkan J Electr Comput Eng"},{"key":"9092_CR70","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09092-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09092-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09092-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T08:06:37Z","timestamp":1704441997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09092-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,16]]},"references-count":70,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["9092"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09092-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,10,16]]},"assertion":[{"value":"21 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}