The DOI matches its Crossref record.
Deep Learning: Foundations and Concepts
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Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They support detecting and classifying individuals even with background noise or occlusions.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [52]
In the citing paper
“Combined with CNNs [52] or other classifiers, background subtraction can improve accuracy by reducing the false positives of nonpassenger objects.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [52]
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Items 201–220 of 220
Uyun, S., Haerani, B. T., Anam, H., & Mujahidah, L. (2026) · International Conference on Smart Computing, IoT and Machine Learning (SIML) · doi:10.1109/siml69834.2026.11621772
Liu, Z., Gan, Y., Ding, C., Wang, Z., Yan, J., Tan, R., Li, Y., Luo, J., & Cui, Y. (2026) · Toxics · doi:10.3390/toxics14080683
Embedding learning on multiplex networks for link prediction
Trautmann, O., Wolkenhauer, O., & Réda, C. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11656-w
Meligy, O. E., & Elmeligy, A. O. (2026) · Dentistry Journal · doi:10.3390/dj14080493
Ying, K. Z., Zainan, N. H., Jusoh, Y. M. M., Hashim, Z., Hi, Y. H., & Sanadi, N. F. A. (2026) · Biointerface Research in Applied Chemistry · doi:10.33263/briac164.109
Bayesian inference for coping with geotechnical model uncertainty: a unified HP2O framework
Cao, Z.-J., Zhang, L.-Y., Shen, M.-Y., Zhang, W., & Zhu, Y. (2026) · Georisk Assessment and Management of Risk for Engineered Systems and Geohazards · doi:10.1080/17499518.2026.2716245
Ali, M., Salman, F., & Alkhatib, M. A. (2026) · The International Journal of Applied Sciences · doi:10.64440/ijas/ijas1004
Mohanan, K. U., Yoo, H., Lessard, B. H., Kymissis, I., & Kim, C. (2026) · Advanced Functional Materials · doi:10.1002/adfm.77874
Instance Reweighting for Adversarial Training Helps Only Against the Training Attack
Scodeller, G., Lazzaro, D., Ledda, E., Ciná, A. E., Biggio, B., Fumera, G., Anguita, D., Roli, F., & Oneto, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38398-3_42
Ramsai, N., Suryanarayana, K., Nair, P., & Sridharan, K. (2026) · IEEE Access · doi:10.1109/access.2026.3735104
Modeling and Forecasting of Urban Housing Tenure Using Deep Learning Approach (2024–2033)
Esmaeili, N., & Ghaffary, G. (2026) · International Conference on Time Series and Forecasting (ITISE) · doi:10.3390/cmsf2026016001
From State to Decision: I-GATE FrameworkPreprint
Faridnia, R. (2026) · Research Square · doi:10.21203/rs.3.rs-10715617/v1
Lello, F., Dida, M. A., Mkoba, E. S., & Mkiramweni, M. E. (2026) · Progress in IS · doi:10.1007/978-3-032-14099-9_39
Xing, S., Han, Q., Charalampidis, E. G., & Lai, Y. (2026) · PRX Intelligence · doi:10.1103/pdkf-98zb
Vilson, Murahartawaty, & Pramesti, D. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715085
Deep Learning of Sequential Data with Transformers and Comparison with Recurrent Neural Networks
Papadimitriou, S. T. (2026) · European Journal of Scientific Research and Reviews. · doi:10.5455/ejsrr.20260311103130
A path to common sense robotics—vision, actions & reasoning
Neuberger, B., Scherl, A., Muster, L., Schwaiger, S., Blaha, A., Sturm, F., Pammer, L., Schebek, M., & Kornfellner, E. (2026) · e+i Elektrotechnik und Informationstechnik · doi:10.1007/s00502-026-01456-z
Deep Belief Forest for Pattern Classification
Han, D., Li, W., & Dezert, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-41960-6_8
Mathematical foundations of artificial intelligence in healthcare: a systematic review
Jiménez-Gaona, Y., & Lakshminarayanan, V. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1995929
Nawata, K. (2026) · Open Journal of Statistics · doi:10.4236/ojs.2026.165024
Metadata
- Authors
- Bishop, C. M., & Bishop, H.
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2024
- OpenAlex
- W4388152645
- Cited by
- 220
- References
- 0
- Citations by year
- 2026: 81 · 2025: 92 · 2024: 47
- FWCI
- 38.61
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- deep learning, deep learning architectures, artificial neural networks, machine learning methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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