{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:23:56Z","timestamp":1773725036458,"version":"3.50.1"},"reference-count":197,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004252","name":"Qatar University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004252","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00521-025-11827-w","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T08:50:46Z","timestamp":1771404646000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comprehensive review of convolutional neural networks: foundations, enhancements and applications"],"prefix":"10.1007","volume":"38","author":[{"given":"Md. Himel","family":"Reza","sequence":"first","affiliation":[]},{"given":"Md. Noman Biswas","family":"Sibly","sequence":"additional","affiliation":[]},{"given":"Shaikh Golam","family":"Rabbani","sequence":"additional","affiliation":[]},{"given":"Shafayetul Huda","family":"Sadi","sequence":"additional","affiliation":[]},{"given":"Md. Faysal","family":"Ahamed","sequence":"additional","affiliation":[]},{"given":"Fariya Bintay","family":"Shafi","sequence":"additional","affiliation":[]},{"given":"Rusab","family":"Sarmun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"11827_CR1","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1113\/JPHYSIOL.1959.SP006308","volume":"148","author":"DH Hubel","year":"1959","unstructured":"Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat\u2019s striate cortex. J Physiol 148:574\u2013591. https:\/\/doi.org\/10.1113\/JPHYSIOL.1959.SP006308","journal-title":"J Physiol"},{"key":"11827_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251\/METRICS","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36:193\u2013202. https:\/\/doi.org\/10.1007\/BF00344251\/METRICS","journal-title":"Biol Cybern"},{"key":"11827_CR3","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/NECO.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541\u2013551. https:\/\/doi.org\/10.1162\/NECO.1989.1.4.541","journal-title":"Neural Comput"},{"key":"11827_CR4","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"11827_CR5","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2016-December. pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"11827_CR6","unstructured":"Tan M, Le Q V. (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: 36th international conference on machine learning, ICML 2019 2019-June. pp 10691\u201310700"},{"key":"11827_CR7","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings - 30th IEEE conference on computer vision and pattern recognition, CVPR 2017 2017-January. pp. 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"11827_CR8","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision 2017-October. pp 618\u2013626. https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"11827_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/S11063-024-11643-8","author":"J Chen","year":"2024","unstructured":"Chen J, Wu P, Zhang X, Xu R, Liang J (2024) Add-Vit: CNN-transformer hybrid architecture for small data paradigm processing. Neural Process Lett. https:\/\/doi.org\/10.1007\/S11063-024-11643-8","journal-title":"Neural Process Lett"},{"key":"11827_CR10","first-page":"3857","volume":"30","author":"S Sabour","year":"2017","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30:3857\u20133867","journal-title":"Adv Neural Inf Process Syst"},{"key":"11827_CR11","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 86:2278\u20132323. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"11827_CR12","unstructured":"Lee D-H (2013) Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks"},{"key":"11827_CR13","first-page":"3320","volume":"4","author":"J Yosinski","year":"2014","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? Adv Neural Inf Process Syst 4:3320\u20133328","journal-title":"Adv Neural Inf Process Syst"},{"key":"11827_CR14","doi-asserted-by":"publisher","unstructured":"Tahmid M, Alam MS, Rao N, Ashrafi KMA (2016) Image-to-image translation with conditional adversarial networks. In: Proceedings of 2023 IEEE 9th international women in engineering (WIE) conference on electrical and computer engineering, WIECON-ECE 2023. pp 468\u2013472. https:\/\/doi.org\/10.1109\/WIECON-ECE60392.2023.10456447","DOI":"10.1109\/WIECON-ECE60392.2023.10456447"},{"key":"11827_CR15","first-page":"5999","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5999\u20136009","journal-title":"Adv Neural Inf Process Syst"},{"key":"11827_CR16","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2016) Densely connected convolutional networks. In: Proceedings - 30th IEEE conference on computer vision and pattern recognition, CVPR 2017 2017. pp 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"11827_CR17","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. https:\/\/doi.org\/10.48550\/arXiv.1804.02767","DOI":"10.48550\/arXiv.1804.02767"},{"key":"11827_CR18","doi-asserted-by":"publisher","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2017) Towards deep learning models resistant to adversarial attacks. In: 6th international conference on learning representations, ICLR 2018 - conference track proceedings. https:\/\/doi.org\/10.48550\/arXiv.1706.06083","DOI":"10.48550\/arXiv.1706.06083"},{"key":"11827_CR19","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le Q V. (2019) EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 10778\u201310787. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"11827_CR20","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16\u00d716 words: transformers for image recognition at scale. In: ICLR 2021 - 9th international conference on learning representations"},{"key":"11827_CR21","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE international conference on computer vision. pp 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11827_CR22","doi-asserted-by":"publisher","unstructured":"Chatterjee S, Tummala P, Speck O, N\u00fcrnberger A (2023) Complex network for complex problems: a comparative study of CNN and complex-valued CNN. In: 5th IEEE international image processing, applications and systems conference, IPAS 2022. https:\/\/doi.org\/10.1109\/IPAS55744.2022.10053060","DOI":"10.1109\/IPAS55744.2022.10053060"},{"key":"11827_CR23","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-031-15893-3_2","volume-title":"New approach to malware detection using optimized convolutional neural network","author":"M Omar","year":"2022","unstructured":"Omar M (2022) New approach to malware detection using optimized convolutional neural network. Springer, New York, pp 13\u201335. https:\/\/doi.org\/10.1007\/978-3-031-15893-3_2"},{"key":"11827_CR24","doi-asserted-by":"crossref","unstructured":"Bougourzi F, Dornaika F, Nakib A, Distante C, Taleb-Ahmed A (2023) 2D and 3D CNN-based fusion approach for COVID-19 severity prediction from 3D CT-scans","DOI":"10.1109\/ICASSPW59220.2023.10192927"},{"key":"11827_CR25","unstructured":"Yunusa \u00ed \u00b5\u00ed H, Qin \u00ed \u00b5\u00ed S, Hamman Adama Chukkol \u00ed \u00b5\u00ed A, Abdu Yusuf \u00ed \u00b5\u00ed A, Bello I, \u00b5\u00ed \u00ed, Lawan \u00ed \u00b5\u00ed A (2024) Exploring the synergies of hybrid CNNs and ViTs architectures for computer vision: a survey"},{"key":"11827_CR26","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"},{"key":"11827_CR27","doi-asserted-by":"publisher","unstructured":"Yamaguchi K, Sakamoto K, Akabane T, Fujimoto Y (1990) A neural network for speaker-independent isolated word recognition. In: 1st international conference on spoken language processing, ICSLP 1990. pp 1077\u20131080. https:\/\/doi.org\/10.21437\/ICSLP.1990-282","DOI":"10.21437\/ICSLP.1990-282"},{"key":"11827_CR28","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume":"8689","author":"MD Zeiler","year":"2014","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. Comput Vision\u2013ECCV 2014 8689:818\u2013833. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","journal-title":"Comput Vision\u2013ECCV 2014"},{"key":"11827_CR29","doi-asserted-by":"publisher","unstructured":"Christlein V, Spranger L, Seuret M, Nicolaou A, Kral P, Maier A (2019) Deep generalized max pooling. In: Proceedings of the international conference on document analysis and recognition, ICDAR. pp 1090\u20131096. https:\/\/doi.org\/10.1109\/ICDAR.2019.00177","DOI":"10.1109\/ICDAR.2019.00177"},{"key":"11827_CR30","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network"},{"key":"11827_CR31","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-319-11740-9_34","volume":"8818","author":"D Yu","year":"2014","unstructured":"Yu D, Wang H, Chen P, Wei Z (2014) Mixed pooling for convolutional neural networks. Lect Notes Comput Sci (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8818:364\u2013375. https:\/\/doi.org\/10.1007\/978-3-319-11740-9_34","journal-title":"Lect Notes Comput Sci (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"11827_CR32","unstructured":"Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. In: 1st international conference on learning representations, ICLR 2013 - conference track proceedings"},{"key":"11827_CR33","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"key":"11827_CR34","first-page":"2449","volume":"28","author":"O Rippel","year":"2015","unstructured":"Rippel O, Snoek J, Adams RP (2015) Spectral representations for convolutional neural networks. Adv Neural Inf Process Syst 28:2449\u20132457","journal-title":"Adv Neural Inf Process Syst"},{"key":"11827_CR35","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8691 LNCS. pp 346\u2013361. https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"11827_CR36","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11827_CR37","doi-asserted-by":"publisher","unstructured":"Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8695 LNCS: pp. 392\u2013407. https:\/\/doi.org\/10.1007\/978-3-319-10584-0_26","DOI":"10.1007\/978-3-319-10584-0_26"},{"key":"11827_CR38","volume-title":"Activation functions in deep learning: a comprehensive survey and benchmark","author":"S Dubey","year":"2022","unstructured":"Dubey S, Singh S, Neurocomputing BC (2022) Activation functions in deep learning: a comprehensive survey and benchmark. Elsevier, Amsterdam"},{"key":"11827_CR39","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: International conference on artificial intelligence and statistics"},{"issue":"6088","key":"11827_CR40","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"key":"11827_CR41","doi-asserted-by":"publisher","unstructured":"Zhang X, Zhang Z, Bae H (2019) Compact cluster-based balanced distribution adaptation for transfer learning. In: Proceedings of the international joint conference on neural networks 2019-July. https:\/\/doi.org\/10.1109\/IJCNN.2019.8852238","DOI":"10.1109\/IJCNN.2019.8852238"},{"key":"11827_CR42","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: International conference on machine learning"},{"key":"11827_CR43","unstructured":"Maas A, Hannun A, icml AN-Proc, 2013 undefined (2013) Rectifier nonlinearities improve neural network acoustic models. awnihannun.comAL Maas, AY Hannun, AY NgProc icml, 2013 awnihannun.com"},{"key":"11827_CR44","doi-asserted-by":"publisher","unstructured":"Xu B, Wang N, Kong H, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. https:\/\/doi.org\/10.48550\/arXiv.1505.00853","DOI":"10.48550\/arXiv.1505.00853"},{"key":"11827_CR45","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, IEEE JS-P (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. openaccess.thecvf.comK He, X Zhang, S Ren, J Sun. In: Proceedings of the IEEE international conference on computer, 2015\u2022openaccess.thecvf.com","DOI":"10.1109\/ICCV.2015.123"},{"key":"11827_CR46","doi-asserted-by":"publisher","unstructured":"Jiang T, Cheng J (2019) Target recognition based on CNN with LeakyReLU and PReLU activation functions. In: Proceedings - 2019 international conference on sensing, diagnostics, prognostics, and control, SDPC 2019. pp 718\u2013722. https:\/\/doi.org\/10.1109\/SDPC.2019.00136","DOI":"10.1109\/SDPC.2019.00136"},{"key":"11827_CR47","unstructured":"Clevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (ELUs). In: 4th international conference on learning representations, ICLR 2016 - conference track proceedings"},{"key":"11827_CR48","doi-asserted-by":"publisher","unstructured":"Grelsson B, Felsberg M (2018) Improved learning in convolutional neural networks with shifted exponential linear units (ShELUs). In: Proceedings - international conference on pattern recognition. pp 517\u2013522. https:\/\/doi.org\/10.1109\/ICPR.2018.8545104","DOI":"10.1109\/ICPR.2018.8545104"},{"key":"11827_CR49","unstructured":"Goodfellow I, Warde-Farley D, Mirza M, Courville AC, Bengio Y (2013) Maxout networks. In: International conference on machine learning"},{"key":"11827_CR50","unstructured":"Springenberg JT, Riedmiller M (2013) Improving deep neural networks with probabilistic maxout units. In: 2nd international conference on learning representations, ICLR 2014 - workshop track proceedings"},{"key":"11827_CR51","unstructured":"Shridhar K, Lee J, Hayashi H, Mehta P, Iwana BK, Kang S, Uchida S, Ahmed S, Dengel A (2019) ProbAct: a probabilistic activation function for deep neural networks"},{"key":"11827_CR52","unstructured":"Ramachandran P, Zoph B, Le Google Brain Q V (2017) Searching for activation functions. In: 6th international conference on learning representations, ICLR 2018 - workshop track proceedings"},{"key":"11827_CR53","doi-asserted-by":"publisher","first-page":"2041","DOI":"10.1109\/TCSS.2022.3179659","volume":"10","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Liu J, Liu G, Wang J, Zhang J (2023) Robustness verification of Swish neural networks embedded in autonomous driving systems. IEEE Trans Comput Soc Syst 10:2041\u20132050. https:\/\/doi.org\/10.1109\/TCSS.2022.3179659","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"11827_CR54","doi-asserted-by":"publisher","unstructured":"Huang YH, Proesmans M, Gool L Van (2023) Padding investigations for CNNs in scene parsing tasks. In: Proceedings of MVA 2023 - 18th international conference on machine vision and applications. https:\/\/doi.org\/10.23919\/MVA57639.2023.10216084","DOI":"10.23919\/MVA57639.2023.10216084"},{"key":"11827_CR55","doi-asserted-by":"publisher","unstructured":"Nguyen AD, Choi S, Kim W, Ahn S, Kim J, Lee S (2019) Distribution padding in convolutional neural networks. In: Proceedings - international conference on image processing, ICIP. pp 4275\u20134279. https:\/\/doi.org\/10.1109\/ICIP.2019.8803537","DOI":"10.1109\/ICIP.2019.8803537"},{"key":"11827_CR56","unstructured":"Mont\u00fafar G, Pascanu R, Cho K, anf Bengio Y (2014) On the number of linear regions of deep neural networks. Adv Neural Inf Process Syst 27"},{"key":"11827_CR57","doi-asserted-by":"crossref","unstructured":"He K and Sun J (2015) Convolutional neural networks at constrained time cost. In:\u00a0Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"11827_CR58","unstructured":"Dumoulin V and Visin F (2016) A guide to convolution arithmetic for deep learning.\u00a0arXiv preprint arXiv:1603.07285"},{"key":"11827_CR59","doi-asserted-by":"publisher","unstructured":"Sainath T, Interspeech CP (2015) Convolutional neural networks for small-footprint keyword spotting. https:\/\/doi.org\/10.21437\/Interspeech.2015-352","DOI":"10.21437\/Interspeech.2015-352"},{"key":"11827_CR60","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"11827_CR61","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/S10710-017-9314-Z","volume":"19","author":"I Goodfellow","year":"2017","unstructured":"Goodfellow I, Bengio Y, Courville A, Heaton J (2017) Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. Genet Program Evol Mach 19:305\u2013307. https:\/\/doi.org\/10.1007\/S10710-017-9314-Z","journal-title":"Genet Program Evol Mach"},{"key":"11827_CR62","first-page":"2563","volume":"12","author":"G Montavon","year":"2011","unstructured":"Montavon G, Braun M (2011) Kernel analysis of deep networks. J Mach Learn Res 12:2563\u20132581","journal-title":"J Mach Learn Res"},{"key":"11827_CR63","unstructured":"Montavon G, Braun ML (2011) Kernel analysis of deep networks klaus-robert Muller *"},{"key":"11827_CR64","doi-asserted-by":"publisher","DOI":"10.1007\/S10791-024-09463-4","author":"M Narkhede","year":"2024","unstructured":"Narkhede M, Mahajan S, Bartakke P, Sutaone M (2024) Towards compressed and efficient CNN architectures via pruning. Discover Comput. https:\/\/doi.org\/10.1007\/S10791-024-09463-4","journal-title":"Discover Comput"},{"key":"11827_CR65","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"key":"11827_CR66","unstructured":"Huang GB, Mattar MA, Berg TL, Learned-Miller E (2008) Labeled faces in the wild: a database for studying face recognition in unconstrained environments"},{"issue":"1","key":"11827_CR67","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"11827_CR68","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/CVPR.2006.100","volume":"2","author":"R Hadsell","year":"2006","unstructured":"Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. Proc IEEE Comput Soc Conf Comput Vision Pattern Recogn 2:1735\u20131742. https:\/\/doi.org\/10.1109\/CVPR.2006.100","journal-title":"Proc IEEE Comput Soc Conf Comput Vision Pattern Recogn"},{"key":"11827_CR69","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1142\/S0218001493000339","volume":"07","author":"J Bromley","year":"1993","unstructured":"Bromley J, Bentz JW, Bottou L, Guyon I, Lecun Y, Moore C, S\u00e4ckinger E, Shah R (1993) Signature verification using a \u201cSIAMESE\u201d time delay neural network. Int J Pattern Recognit Artif Intell 07:669\u2013688. https:\/\/doi.org\/10.1142\/S0218001493000339","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"11827_CR70","unstructured":"Hermans A, Beyer L, Leibe B (2017) Defense of the triplet loss for person re-identification"},{"key":"11827_CR71","doi-asserted-by":"crossref","unstructured":"Parkhi O, Vedaldi A, Zisserman A (2015) Deep face recognition. In: BMVC 2015-proceedings of the british machine vision conference 2015. British Machine Vision Association","DOI":"10.5244\/C.29.41"},{"key":"11827_CR72","doi-asserted-by":"publisher","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: 2nd international conference on learning representations, ICLR 2014 - conference track proceedings. https:\/\/doi.org\/10.61603\/ceas.v2i1.33","DOI":"10.61603\/ceas.v2i1.33"},{"key":"11827_CR73","unstructured":"Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. In: 31st international conference on machine learning, vol 4. ICML, pp 3057\u20133070"},{"key":"11827_CR74","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3481640","author":"S Bhakta","year":"2024","unstructured":"Bhakta S, Nandi U, Mahapatra KR, Singh MM, Noorwali A (2024) SWOSBC: a novel optimizer for learning convolutional neural network. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3481640","journal-title":"IEEE Access"},{"key":"11827_CR75","unstructured":"Ruder S (2016) An overview of gradient descent optimization algorithms"},{"key":"11827_CR76","doi-asserted-by":"publisher","unstructured":"Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT 2010 - 19th international conference on computational statistics, Keynote, invited and contributed papers. pp 177\u2013186. https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16","DOI":"10.1007\/978-3-7908-2604-3_16"},{"issue":"1","key":"11827_CR77","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12(1):145\u2013151","journal-title":"Neural Netw"},{"key":"11827_CR78","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"11827_CR79","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/CTEMS.2018.8769211","volume":"2018","author":"EM Dogo","year":"2018","unstructured":"Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO (2018) A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. Proc Int Conf Comput Tech Electron Mech Syst CTEMS 2018:92\u201399. https:\/\/doi.org\/10.1109\/CTEMS.2018.8769211","journal-title":"Proc Int Conf Comput Tech Electron Mech Syst CTEMS"},{"key":"11827_CR80","unstructured":"Zeiler MD (2012) ADADELTA: an adaptive learning rate method"},{"key":"11827_CR81","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations. ICLR 2015 - conference track proceedings"},{"key":"11827_CR82","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11827_CR83","doi-asserted-by":"publisher","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings - 30th IEEE conference on computer vision and pattern recognition, CVPR. pp 5987\u20135995. https:\/\/doi.org\/10.1109\/CVPR.2017.634","DOI":"10.1109\/CVPR.2017.634"},{"key":"11827_CR84","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution","DOI":"10.1109\/CVPR.2018.00262"},{"key":"11827_CR85","doi-asserted-by":"crossref","unstructured":"Zoph B, Brain G, Vasudevan V, Shlens J, Le Google Brain QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2018.00907"},{"key":"11827_CR86","unstructured":"Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions. In: 4th international conference on learning representations, ICLR 2016 - conference track proceedings"},{"key":"11827_CR87","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Chen T et al (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"key":"11827_CR88","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"11827_CR89","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"11827_CR90","unstructured":"Visin F, Romero A, Ciccone M, Kastner K, Cho K, Matteucci M, Bengio Y, Courville A Reseg: a recurrent neural network-based model for semantic segmentation. cv-foundation.org"},{"key":"11827_CR91","doi-asserted-by":"publisher","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. In: 2nd international conference on learning representations, ICLR 2014 - conference track proceedings. https:\/\/doi.org\/10.48550\/arXiv.1312.4400","DOI":"10.48550\/arXiv.1312.4400"},{"key":"11827_CR92","unstructured":"Gholamalinezhad H, Khosravi H (2020) Pooling methods in deep neural networks, a review"},{"key":"11827_CR93","unstructured":"Sermanet P, Chintala S (2012) Convolutional neural networks applied to house numbers digit classification. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012)"},{"key":"11827_CR94","doi-asserted-by":"crossref","unstructured":"Kwak S, Hong S (2017) Weakly supervised semantic segmentation using superpixel pooling network. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.11213"},{"key":"11827_CR95","doi-asserted-by":"publisher","unstructured":"Rasamoelina AD, Adjailia F, Sincak P (2020) A review of activation function for artificial neural network. In: SAMI 2020 - IEEE 18th world symposium on applied machine intelligence and informatics, proceedings. pp 281\u2013286. https:\/\/doi.org\/10.1109\/SAMI48414.2020.9108717","DOI":"10.1109\/SAMI48414.2020.9108717"},{"key":"11827_CR96","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/J.NEUCOM.2022.06.111","volume":"503","author":"SR Dubey","year":"2022","unstructured":"Dubey SR, Singh SK, Chaudhuri BB (2022) Activation functions in deep learning: a comprehensive survey and benchmark. Neurocomputing 503:92\u2013108. https:\/\/doi.org\/10.1016\/J.NEUCOM.2022.06.111","journal-title":"Neurocomputing"},{"issue":"12","key":"11827_CR97","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33(12):6999\u20137019","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11827_CR98","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/CR030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79\u201382. https:\/\/doi.org\/10.3354\/CR030079","journal-title":"Clim Res"},{"key":"11827_CR99","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical. Learning. https:\/\/doi.org\/10.1007\/978-0-387-84858-7","journal-title":"Learning"},{"key":"11827_CR100","doi-asserted-by":"publisher","unstructured":"(2007) Pattern recognition and machine learning. J Electron Imaging 16:049901. https:\/\/doi.org\/10.1117\/1.2819119","DOI":"10.1117\/1.2819119"},{"key":"11827_CR101","unstructured":"Guo C, Pleiss G, Sun Y, Weinberger KQ (2017) On calibration of modern neural networks. In: 34th international conference on machine learning, vol 3. ICML, pp 2130\u20132143"},{"key":"11827_CR102","doi-asserted-by":"publisher","first-page":"5962","DOI":"10.1109\/TPAMI.2021.3087709","volume":"44","author":"J Deng","year":"2022","unstructured":"Deng J, Guo J, Yang J, Xue N, Kotsia I, Zafeiriou S (2022) ArcFace: additive angular margin loss for deep face recognition. IEEE Trans Pattern Anal Mach Intell 44:5962\u20135979. https:\/\/doi.org\/10.1109\/TPAMI.2021.3087709","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11827_CR103","doi-asserted-by":"publisher","first-page":"3988","DOI":"10.1609\/AAAI.V31I1.11201","volume":"31","author":"W Chen","year":"2017","unstructured":"Chen W, Chen X, Zhang J, Huang K (2017) A multi-task deep network for person re-identification. Proc AAAI Conf Artif Intell 31:3988\u20133994. https:\/\/doi.org\/10.1609\/AAAI.V31I1.11201","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"11827_CR104","doi-asserted-by":"crossref","unstructured":"Xie X (2024) A hybrid CNN-LSTM architecture for enhanced music genre classification. In: 2024 5th international conference on machine learning and computer application (ICMLCA)","DOI":"10.1109\/ICMLCA63499.2024.10753693"},{"issue":"3","key":"11827_CR105","doi-asserted-by":"publisher","first-page":"032102","DOI":"10.1088\/2631-8695\/ad6ca7","volume":"6","author":"BA Demiss","year":"2024","unstructured":"Demiss BA, Elsaigh WA (2024) Application of novel hybrid deep learning architectures combining convolutional neural networks (CNN) and recurrent neural networks (RNN): construction duration estimates prediction considering preconstruction uncertainties. Eng Res Express 6(3):032102. https:\/\/doi.org\/10.1088\/2631-8695\/ad6ca7","journal-title":"Eng Res Express"},{"key":"11827_CR106","doi-asserted-by":"crossref","unstructured":"Nandhini I, Prasanth LL, Nagalakshmi T, and Manjula D (2024) Integrating convolutional and recurrent networks for image caption generation: a unified approach. In: 2024 15th international conference on computing communication and networking technologies (ICCCNT). pp 1\u20135. IEEE","DOI":"10.1109\/ICCCNT61001.2024.10724799"},{"issue":"1","key":"11827_CR107","doi-asserted-by":"publisher","first-page":"1","DOI":"10.24167\/proxies.v8i1.12473","volume":"8","author":"APP Sitanggang","year":"2024","unstructured":"Sitanggang APP (2024) Hybrid CNN and RNNS model for sentiment analysis. Proxies J Inf 8(1):1\u201312","journal-title":"Proxies J Inf"},{"key":"11827_CR108","doi-asserted-by":"crossref","unstructured":"Cao Z, Tang S (2024) IRNet: an information retrieval model based on dual-stream fusion of CNN and transformer. In: 2024 5th international conference on machine learning and computer application","DOI":"10.1109\/ICMLCA63499.2024.10753958"},{"key":"11827_CR109","doi-asserted-by":"publisher","first-page":"4072","DOI":"10.1109\/TCSVT.2024.3523316","volume":"35","author":"L Wu","year":"2024","unstructured":"Wu L, Zhang M, Piao Y, Yao Z, Sun W, Tian F, Lu H (2024) CNN-transformer rectified collaborative learning for medical image segmentation. IEEE Trans Circ Syst Video Technol 35:4072\u20134086","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"11827_CR110","unstructured":"Pereira GA, Hussain M (2024) A review of transformer-based models for computer vision tasks: capturing global context and spatial relationships"},{"key":"11827_CR111","doi-asserted-by":"publisher","first-page":"7982","DOI":"10.1007\/s10489-024-05603-x","volume":"54","author":"M Jung","year":"2024","unstructured":"Jung M, Lee J, Intelligence JK-A (2024) A lightweight CNN-transformer model for learning traveling salesman problems. Appl Intell 54:7982\u20137993. https:\/\/doi.org\/10.1007\/s10489-024-05603-x","journal-title":"Appl Intell"},{"key":"11827_CR112","doi-asserted-by":"crossref","unstructured":"Vats S, Singh CR, and Mehta S (2024) Next-generation diagnostic tools: the role of hybrid CNN-transformer models in liver cancer detection. In: 2024 15th international conference on computing communication and networking technologies (ICCCNT). IEEE, pp 1\u20135","DOI":"10.1109\/ICCCNT61001.2024.10723884"},{"key":"11827_CR113","unstructured":"Mehta S, Rastegari M (2021) MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. In: ICLR 2022 - 10th international conference on learning representations"},{"key":"11827_CR114","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418","author":"MM Bronstein","year":"2016","unstructured":"Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P (2016) Geometric deep learning: going beyond Euclidean data. IEEE Signal Process Mag. https:\/\/doi.org\/10.1109\/MSP.2017.2693418","journal-title":"IEEE Signal Process Mag"},{"key":"11827_CR115","unstructured":"Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, Gulcehre C, Song F, Ballard A, Gilmer J, Dahl G, Vaswani A, Allen K, Nash C, Langston V, Dyer C, Heess N, Wierstra D, Kohli P, Botvinick M, Vinyals O, Li Y, Pascanu R (2018) Relational inductive biases, deep learning, and graph networks"},{"key":"11827_CR116","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks"},{"key":"11827_CR117","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386","author":"Z Wu","year":"2019","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11827_CR118","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D (2018) Disease prediction using graph convolutional networks: application to Autism Spectrum Disorder and Alzheimer\u2019s disease. Med Image Anal 48:117\u2013130. https:\/\/doi.org\/10.1016\/j.media.2018.06.001","journal-title":"Med Image Anal"},{"key":"11827_CR119","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: deep learning on point sets for 3D classification and segmentation"},{"key":"11827_CR120","doi-asserted-by":"publisher","DOI":"10.1145\/3326362","author":"Y Wang","year":"2019","unstructured":"Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph CNN for learning on point clouds. ACM Trans Graph. https:\/\/doi.org\/10.1145\/3326362","journal-title":"ACM Trans Graph"},{"key":"11827_CR121","doi-asserted-by":"crossref","unstructured":"Zhang Y, Rabbat M (2018) A graph-CNN for 3D point cloud classification","DOI":"10.1109\/ICASSP.2018.8462291"},{"key":"11827_CR122","doi-asserted-by":"crossref","unstructured":"Hu Q, Yang B, Xie L, Rosa S, Guo Y, Wang Z, Trigoni N, Markham A (2019) RandLA-Net: efficient semantic segmentation of large-scale point clouds","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"11827_CR123","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs"},{"key":"11827_CR124","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch geometric"},{"key":"11827_CR125","doi-asserted-by":"publisher","first-page":"16591","DOI":"10.1109\/ACCESS.2021.3053408","volume":"9","author":"W Weng","year":"2015","unstructured":"Weng W, Zhu X (2015) U-Net: convolutional networks for biomedical image segmentation. IEEE Access 9:16591\u201316603. https:\/\/doi.org\/10.1109\/ACCESS.2021.3053408","journal-title":"IEEE Access"},{"key":"11827_CR126","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AAA Setio","year":"2016","unstructured":"Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, Van Ginneken B (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35:1160\u20131169. https:\/\/doi.org\/10.1109\/TMI.2016.2536809","journal-title":"IEEE Trans Med Imaging"},{"key":"11827_CR127","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, and Darrell T (2015) Fully convolutional networks for semantic segmentation","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"11827_CR128","doi-asserted-by":"publisher","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 3354\u20133361. https:\/\/doi.org\/10.1109\/CVPR.2012.6248074","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"11827_CR129","doi-asserted-by":"publisher","DOI":"10.1016\/J.BSPC.2025.108202","volume":"110","author":"MR Islam","year":"2025","unstructured":"Islam MR, Ahamed MF, Islam MR, Nahiduzzaman M, Ahsan M (2025) Detection, localization, segmentation, and classification in colorectal cancer screening using deep learning: a systematic review. Biomed Signal Process Control 110:108202. https:\/\/doi.org\/10.1016\/J.BSPC.2025.108202","journal-title":"Biomed Signal Process Control"},{"key":"11827_CR130","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2024.124908","volume":"256","author":"MF Ahamed","year":"2024","unstructured":"Ahamed MF, Nahiduzzaman M, Islam MR, Naznine M, Arselene Ayari M, Khandakar A, Haider J (2024) Detection of various gastrointestinal tract diseases through a deep learning method with ensemble ELM and explainable AI. Expert Syst Appl 256:124908. https:\/\/doi.org\/10.1016\/J.ESWA.2024.124908","journal-title":"Expert Syst Appl"},{"key":"11827_CR131","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPBIOMED.2024.109503","volume":"185","author":"MF Ahamed","year":"2025","unstructured":"Ahamed MF, Shafi FB, Nahiduzzaman M, Ayari MA, Khandakar A (2025) Interpretable deep learning architecture for gastrointestinal disease detection: a Tri-stage approach with PCA and XAI. Comput Biol Med 185:109503. https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2024.109503","journal-title":"Comput Biol Med"},{"key":"11827_CR132","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-90851-1","author":"MF Ahamed","year":"2025","unstructured":"Ahamed MF, Nahiduzzaman M, Mahmud G, Shafi FB, Ayari MA, Khandakar A, Abdullah-Al-Wadud M, Islam SMR (2025) Improving Malaria diagnosis through interpretable customized CNNs architectures. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-025-90851-1","journal-title":"Sci Rep"},{"key":"11827_CR133","doi-asserted-by":"publisher","DOI":"10.3390\/technologies11050134","author":"O Sarkar","year":"2023","unstructured":"Sarkar O, Islam MR, Syfullah MK, Islam MT, Ahamed MF, Ahsan M, Haider J (2023) Multi-scale CNN: an explainable AI-integrated unique deep learning framework for lung-affected disease classification. Technologies. https:\/\/doi.org\/10.3390\/technologies11050134","journal-title":"Technologies"},{"key":"11827_CR134","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21608\/IJT.2025.346338.1071","volume":"05","author":"MB Badawi","year":"2025","unstructured":"Badawi MB, Salah B, Fawaz W (2025) Deep learning-based pneumonia detection using chest x-ray images. Int J Telecommun 05:1\u201316. https:\/\/doi.org\/10.21608\/IJT.2025.346338.1071","journal-title":"Int J Telecommun"},{"key":"11827_CR135","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21608\/IJT.2024.280957.1045","volume":"04","author":"M Badawi","year":"2024","unstructured":"Badawi M, Elgohary R, Tarek M, EzzAlRegal M, Ahmed A, Samir A, Ehab N (2024) Skin cancer classification and segmentation using deep learning. Int J Telecommun 04:1\u201323. https:\/\/doi.org\/10.21608\/IJT.2024.280957.1045","journal-title":"Int J Telecommun"},{"key":"11827_CR136","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-59967-5_8","author":"MF Ahamed","year":"2024","unstructured":"Ahamed MF et al (2024) Interpretable deep learning model for tuberculosis detection using X-ray images. Surveillance Prevent Control Infect Dis. https:\/\/doi.org\/10.1007\/978-3-031-59967-5_8","journal-title":"Surveillance Prevent Control Infect Dis"},{"key":"11827_CR137","doi-asserted-by":"publisher","DOI":"10.3390\/TECHNOLOGIES12090151","volume":"12","author":"MdM Hossain","year":"2024","unstructured":"Hossain MdM, Islam MdR, Ahamed MdF, Ahsan M, Haider J (2024) A collaborative federated learning framework for lung and colon cancer classifications. Technologies 12:151. https:\/\/doi.org\/10.3390\/TECHNOLOGIES12090151","journal-title":"Technologies"},{"key":"11827_CR138","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"11827_CR139","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11827_CR140","doi-asserted-by":"crossref","unstructured":"Ku J, Mozifian M, Lee J, Harakeh A, and Waslander SL (2018) Joint 3d proposal generation and object detection from view aggregation. In: 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1\u20138","DOI":"10.1109\/IROS.2018.8594049"},{"key":"11827_CR141","doi-asserted-by":"publisher","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP 2014 - 2014 conference on empirical methods in natural language processing, proceedings of the conference. pp 1746\u20131751. https:\/\/doi.org\/10.3115\/v1\/d14-1181","DOI":"10.3115\/v1\/d14-1181"},{"key":"11827_CR142","unstructured":"Dos Santos C and Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers. pp 69\u201378"},{"key":"11827_CR143","unstructured":"Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. In: International conference on machine learning, pp 1243\u20131252"},{"key":"11827_CR144","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, and Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"key":"11827_CR145","doi-asserted-by":"crossref","unstructured":"Carreira J and Zisserman A (2017) Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"11827_CR146","doi-asserted-by":"crossref","unstructured":"Feichtenhofer C, Pinz A, and Zisserman A (2016) Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1933\u20131941","DOI":"10.1109\/CVPR.2016.213"},{"key":"11827_CR147","doi-asserted-by":"crossref","unstructured":"Sultani W, Chen C, and Shah M (2018) Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 6479\u20136488","DOI":"10.1109\/CVPR.2018.00678"},{"key":"11827_CR148","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27"},{"key":"11827_CR149","unstructured":"Karras T, Aila T, Laine S, and Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv:1710.10196"},{"key":"11827_CR150","doi-asserted-by":"publisher","DOI":"10.55730\/1300-0632.3977","author":"H Ar\u011fun","year":"2023","unstructured":"Ar\u011fun H (2023) Variational autoencoder-based anomaly detection in time series data for inventory record inaccuracy. Turk J Electr Eng Comput Sci. https:\/\/doi.org\/10.55730\/1300-0632.3977","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"11827_CR151","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Shi W et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"11827_CR152","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11352-0_207","author":"JO Michel","year":"2019","unstructured":"Michel JO (2019) Deep learning on sustainable development. Encycl Sustain Higher Educ. https:\/\/doi.org\/10.1007\/978-3-030-11352-0_207","journal-title":"Encycl Sustain Higher Educ"},{"key":"11827_CR153","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70\u201390","journal-title":"Comput Electron Agric"},{"issue":"7051","key":"11827_CR154","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/nature03906","volume":"436","author":"K Emanuel","year":"2005","unstructured":"Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30\u2009years. Nature 436(7051):686\u2013688. https:\/\/doi.org\/10.1038\/nature03906","journal-title":"Nature"},{"key":"11827_CR155","doi-asserted-by":"publisher","first-page":"18445","DOI":"10.1007\/s00521-024-10152-y","volume":"36","author":"MdF Ahamed","year":"2024","unstructured":"Ahamed MdF, Salam A, Nahiduzzaman Md, Abdullah-Al-Wadud M, Islam SMR (2024) Streamlining plant disease diagnosis with convolutional neural networks and edge devices. Neural Comput Appl 36:18445\u201318477. https:\/\/doi.org\/10.1007\/s00521-024-10152-y","journal-title":"Neural Comput Appl"},{"key":"11827_CR156","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2025.113028","volume":"310","author":"M Nahiduzzaman","year":"2025","unstructured":"Nahiduzzaman M, Ahamed MF, Naznine M, Karim MJ, Kibria HB, Ayari MA, Khandakar A, Ashraf A, Ahsan M, Haider J (2025) An automated waste classification system using deep learning techniques: toward efficient waste recycling and environmental sustainability. Knowl Based Syst 310:113028. https:\/\/doi.org\/10.1016\/J.KNOSYS.2025.113028","journal-title":"Knowl Based Syst"},{"key":"11827_CR157","doi-asserted-by":"publisher","first-page":"E5716","DOI":"10.1073\/pnas.1719367115","volume":"115","author":"M Norouzzadeh","year":"2018","unstructured":"Norouzzadeh M, Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C, Clune J (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc Natl Acad Sci U S A 115:E5716\u2013E5725. https:\/\/doi.org\/10.1073\/pnas.1719367115","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"4","key":"11827_CR158","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2766959","volume":"34","author":"S Bell","year":"2015","unstructured":"Bell S, Bala K (2015) Learning visual similarity for product design with convolutional neural networks. ACM Trans Graphcs (TOG) 34(4):1\u201310","journal-title":"ACM Trans Graphcs (TOG)"},{"key":"11827_CR159","doi-asserted-by":"crossref","unstructured":"Han X, Wu Z, Wu Z, Yu R, and Davis LS (2018) Viton: an image-based virtual try-on network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7543\u20137552","DOI":"10.1109\/CVPR.2018.00787"},{"key":"11827_CR160","doi-asserted-by":"crossref","unstructured":"Covington P, Adams J, and Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems. pp 191\u2013198","DOI":"10.1145\/2959100.2959190"},{"key":"11827_CR161","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: 4th international conference on learning representations, ICLR 2016 - conference track proceedings"},{"key":"11827_CR162","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2014","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L, Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2014) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"11827_CR163","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.neucom.2017.09.061","volume":"275","author":"Z Wang","year":"2018","unstructured":"Wang Z, Wang X, Wang G (2018) Learning fine-grained features via a CNN tree for large-scale classification. Neurocomputing 275:1231\u20131240","journal-title":"Neurocomputing"},{"key":"11827_CR164","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patrec.2015.10.013","volume":"81","author":"M Minervini","year":"2016","unstructured":"Minervini M, Fischbach A, Scharr H, Tsaftaris SA (2016) Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn Lett 81:80\u201389","journal-title":"Pattern Recogn Lett"},{"key":"11827_CR165","doi-asserted-by":"crossref","unstructured":"Branson S, Van Horn G, Belongie S, Perona P (2014) Bird species categorization using pose normalized deep convolutional nets. In: BMVC 2014 - proceedings of the British machine vision conference 2014","DOI":"10.5244\/C.28.87"},{"key":"11827_CR166","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.patcog.2016.03.023","volume":"59","author":"Q Zhou","year":"2016","unstructured":"Zhou Q, Zheng B, Zhu W, Latecki LJ (2016) Multi-scale context for scene labeling via flexible segmentation graph. Pattern Recogn 59:312\u2013324","journal-title":"Pattern Recogn"},{"key":"11827_CR167","doi-asserted-by":"publisher","unstructured":"Shuai B, Wang G, Zuo Z, Wang B, Zhao L (2015) Integrating parametric and non-parametric models for scene labeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4249\u20134258. https:\/\/doi.org\/10.1109\/CVPR.2015.7299053","DOI":"10.1109\/CVPR.2015.7299053"},{"key":"11827_CR168","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1109\/LSP.2015.2441781","volume":"22","author":"B Shuai","year":"2015","unstructured":"Shuai B, Zuo Z, Wang G (2015) Quaddirectional 2D-recurrent neural networks for image labeling. IEEE Signal Process Lett 22:1990\u20131994. https:\/\/doi.org\/10.1109\/LSP.2015.2441781","journal-title":"IEEE Signal Process Lett"},{"key":"11827_CR169","doi-asserted-by":"publisher","unstructured":"Mostajabi M, Yadollahpour P, Shakhnarovich G (2014) Feedforward semantic segmentation with zoom-out features. In: Proceedings of the ieee computer society conference on computer vision and pattern recognition. pp 3376\u20133385. https:\/\/doi.org\/10.1109\/CVPR.2015.7298959","DOI":"10.1109\/CVPR.2015.7298959"},{"key":"11827_CR170","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFs. 1\u201312"},{"key":"11827_CR171","doi-asserted-by":"publisher","first-page":"3542","DOI":"10.1016\/J.PATCOG.2015.04.018","volume":"48","author":"Y Li","year":"2015","unstructured":"Li Y, Wang S, Tian Q, Ding X (2015) Feature representation for statistical-learning-based object detection: a review. Pattern Recognit 48:3542\u20133559. https:\/\/doi.org\/10.1016\/J.PATCOG.2015.04.018","journal-title":"Pattern Recognit"},{"key":"11827_CR172","doi-asserted-by":"publisher","unstructured":"Girshick R, Iandola F, Darrell T, Malik J (2014) Deformable part models are convolutional neural networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 437\u2013446. https:\/\/doi.org\/10.1109\/CVPR.2015.7298641","DOI":"10.1109\/CVPR.2015.7298641"},{"key":"11827_CR173","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/S11263-013-0620-5","volume":"104","author":"JRR Uijlings","year":"2013","unstructured":"Uijlings JRR, Van De Sande KEA, Gevers T, Smeulders AWM (2013) Selective search for object recognition. Int J Comput Vis 104:154\u2013171. https:\/\/doi.org\/10.1007\/S11263-013-0620-5","journal-title":"Int J Comput Vis"},{"key":"11827_CR174","doi-asserted-by":"publisher","unstructured":"Zhang C, Yao C, Shi B, Bai X (2015) Automatic discrimination of text and non-text natural images. In: Proceedings of the international conference on document analysis and recognition, ICDAR. pp 886\u2013890. https:\/\/doi.org\/10.1109\/ICDAR.2015.7333889","DOI":"10.1109\/ICDAR.2015.7333889"},{"key":"11827_CR175","unstructured":"Goodfellow IJ, Bulatov Y, Ibarz J, Arnoud S, Shet V (2013) Multi-digit number recognition from street view imagery using deep convolutional neural networks. In: 2nd international conference on learning representations, ICLR 2014 - conference track proceedings"},{"key":"11827_CR176","unstructured":"Jaderberg M, Simonyan K, Vedaldi A, Zisserman A (2014) Deep structured output learning for unconstrained text recognition. In: 3rd international conference on learning representations, ICLR 2015 - conference track proceedings"},{"key":"11827_CR177","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1007\/978-3-319-10593-2_34","volume":"8692","author":"M Jaderberg","year":"2014","unstructured":"Jaderberg M, Vedaldi A, Zisserman A (2014) Deep features for text spotting. Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8692:512\u2013528. https:\/\/doi.org\/10.1007\/978-3-319-10593-2_34","journal-title":"Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"11827_CR178","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/J.PATCOG.2011.05.017","volume":"45","author":"AM Tousch","year":"2012","unstructured":"Tousch AM, Herbin S, Audibert JY (2012) Semantic hierarchies for image annotation: a survey. Pattern Recognit 45:333\u2013345. https:\/\/doi.org\/10.1016\/J.PATCOG.2011.05.017","journal-title":"Pattern Recognit"},{"key":"11827_CR179","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/J.NEUCOM.2017.09.061","volume":"275","author":"Z Wang","year":"2018","unstructured":"Wang Z, Wang X, Wang G (2018) Learning fine-grained features via a CNN tree for Large-scale classification. Neurocomputing 275:1231\u20131240. https:\/\/doi.org\/10.1016\/J.NEUCOM.2017.09.061","journal-title":"Neurocomputing"},{"key":"11827_CR180","doi-asserted-by":"crossref","unstructured":"Yan Z, Jagadeesh V, Decoste D, Di W, Piramuthu R (2015) HD-CNN: hierarchical deep convolutional neural network for image classification","DOI":"10.1109\/ICCV.2015.314"},{"key":"11827_CR181","doi-asserted-by":"crossref","unstructured":"Berg T, Liu J, Woo Lee S, Alexander ML, Jacobs DW, Belhumeur PN (2014) Birdsnap: large-scale fine-grained visual categorization of birds. 2011\u20132018","DOI":"10.1109\/CVPR.2014.259"},{"key":"11827_CR182","doi-asserted-by":"publisher","unstructured":"Yang L, Luo P, Loy CC, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pp 3973\u20133981. https:\/\/doi.org\/10.1109\/CVPR.2015.7299023","DOI":"10.1109\/CVPR.2015.7299023"},{"key":"11827_CR183","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/J.PATCOG.2017.06.002","volume":"71","author":"GS Xie","year":"2017","unstructured":"Xie GS, Zhang XY, Yang W, Xu M, Yan S, Liu CL (2017) LG-CNN: from local parts to global discrimination for fine-grained recognition. Pattern Recognit 71:118\u2013131. https:\/\/doi.org\/10.1016\/J.PATCOG.2017.06.002","journal-title":"Pattern Recognit"},{"key":"11827_CR184","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/978-3-319-10590-1_54","volume":"8689","author":"N Zhang","year":"2014","unstructured":"Zhang N, Donahue J, Girshick R, Darrell T (2014) Part-based R-CNNs for fine-grained category detection. Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8689:834\u2013849. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_54","journal-title":"Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"11827_CR185","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/J.PATCOG.2015.08.027","volume":"51","author":"DT Nguyen","year":"2016","unstructured":"Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recognit 51:148\u2013175. https:\/\/doi.org\/10.1016\/J.PATCOG.2015.08.027","journal-title":"Pattern Recognit"},{"key":"11827_CR186","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume":"8693","author":"TY Lin","year":"2014","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: common objects in context. Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8693:740\u2013755. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","journal-title":"Lect Notes Comput Sci (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"11827_CR187","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. In: 3rd international conference on learning representations, ICLR 2015 - conference track proceedings"},{"key":"11827_CR188","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/S11263-019-01228-7\/FIGURES\/21","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128:336\u2013359. https:\/\/doi.org\/10.1007\/S11263-019-01228-7\/FIGURES\/21","journal-title":"Int J Comput Vis"},{"key":"11827_CR189","doi-asserted-by":"publisher","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. pp 1135\u20131144. https:\/\/doi.org\/10.1145\/2939672.2939778\/SUPPL_FILE\/KDD2016_RIBEIRO_ANY_CLASSIFIER_01-ACM.MP4","DOI":"10.1145\/2939672.2939778\/SUPPL_FILE\/KDD2016_RIBEIRO_ANY_CLASSIFIER_01-ACM.MP4"},{"key":"11827_CR190","unstructured":"Lundberg SM, Allen PG, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30"},{"issue":"7639","key":"11827_CR191","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115\u2013118","journal-title":"Nature"},{"key":"11827_CR192","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. In: International conference on machine learning, pp 3319\u20133328"},{"key":"11827_CR193","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1609\/AIMAG.V38I3.2741","volume":"38","author":"B Goodman","year":"2017","unstructured":"Goodman B, Flaxman S (2017) European Union regulations on algorithmic decision-making and a \u201cright to explanation.\u201d AI Mag 38:50\u201357. https:\/\/doi.org\/10.1609\/AIMAG.V38I3.2741","journal-title":"AI Mag"},{"key":"11827_CR194","doi-asserted-by":"publisher","unstructured":"Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an overview of interpretability of machine learning. In: Proceedings - 2018 IEEE 5th international conference on data science and advanced analytics, DSAA 2018. pp 80\u201389. https:\/\/doi.org\/10.1109\/DSAA.2018.00018","DOI":"10.1109\/DSAA.2018.00018"},{"key":"11827_CR195","unstructured":"Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning"},{"key":"11827_CR196","unstructured":"Lee, C.Y., Gallagher, P.W. and Tu, Z., 2016, May. Generalizing pooling functions in convolutional neural networks: Mixed, gated, andtree. In Artificial intelligence and statistics (pp. 464-472). PMLR."},{"key":"11827_CR197","unstructured":"Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances inneural information processing systems, 25."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11827-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11827-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11827-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:43:07Z","timestamp":1773718987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11827-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":197,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11827"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11827-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"22 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2026","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"56"}}