Cited by 810Neural Networks and ApplicationsNeural dynamics and brain functionMachine Learning and ELM

The DOI matches its Crossref record.

Neural Networks

Rojas, Raúl·1996·Springer eBooks

DOI 10.1007/978-3-642-61068-4

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-3-642-61068-4

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Sorted by citations

Items 401–500 of 809

Prediction of high energy particle shower sizes and core location using artificial neural networks
Devi, G., Sarma, K. K., Datta, P., et al. (2012) · Indian Journal of Physics · doi:10.1007/s12648-012-0007-4 · Cited by 8
Application of Artificial Neural Networks and Principal Component Analysis to Predict Results of Infertility Treatment Using the IVF Method
Milewski, R. J., Jankowska, D., Cwalina, U., et al. (2016) · Studies in Logic Grammar and Rhetoric · doi:10.1515/slgr-2016-0045 · Cited by 8
Analytical performance assessment and high-throughput low-latency spike routing algorithm for spiking neural network systems
Vu, H.-T., Okuyama, Y., & Abdallah, A. B. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02792-y · Cited by 8
Determining the Parameters of the Mathematical Model of the Process of Searching for Harmful Information
Kotenko, I. V., & Parashchuk, I. B. (2019) · Studies in systems, decision and control · doi:10.1007/978-3-030-32648-7_18 · Cited by 8
Turing: Then, Now and Still Key
Greer, K. (2013) · Cited by 8
IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
Radonjić, M., Vujnović, S., Krstić, A., et al. (2022) · Applied Sciences · doi:10.3390/app12094385 · Cited by 8
Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm
Al-Jarrah, M. A., Al-Jarrah, A., Jarrah, A. A., et al. (2022) · Revue d intelligence artificielle · doi:10.18280/ria.360601 · Cited by 8
Neuroadaptive Fault-Tolerant Control Embedded With Diversified Activating Functions With Application to Auto-Driving Vehicles Under Fading Actuation
Gao, Z., Yu, W., & Yan, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3248100 · Cited by 8
Benefits of a robotic chamber system for determining evapotranspiration in an erosion-affected, heterogeneous cropland
Dahlmann, A., Hoffmann, M., Verch, G., Schmidt, M., Sommer, M. G., Augustin, J., & Dubbert, M. (2023) · Hydrology and earth system sciences · doi:10.5194/hess-27-3851-2023 · Cited by 8
Robustness Analysis of Eleven Linear Classifiers in Extremely High–Dimensional Feature Spaces
Lausser, L. M., & Kestler, H. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12159-3_7 · Cited by 10
Ionospheric Tomography by Neural Network Collocation Method
Takeda, T., & Ma, X. (2007) · Plasma and Fusion Research · doi:10.1585/pfr.2.s1015 · Cited by 8
The possible association between summer monsoon rainfall in India and sunspot numbers
Chattopadhyay, S., & Chattopadhyay, G. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2010.517800 · Cited by 8
Handling Conjunctions in Named Entities
Dale, R., & Mazur, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-70939-8_12 · Cited by 8
Ground Truth Data, Content, Metrics, and Analysis
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_7 · Cited by 8
Neural network-based detection of virtual environment anomalies
Nantes, A., Brown, R. A., & Maire, F. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1132-x · Cited by 8
A Complex Systems Approach to Infectious Disease Surveillance and Response
Shi, B., Xia, S., & Liu, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02753-1_53 · Cited by 8
Separation of hadronic W-decays from QCD-background with self-organizing maps
Becks, K. H., Drees, J., Flagmeyer, U., et al. (1999) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/s0168-9002(99)00049-2 · Cited by 8
Use of Neural Net Models to Forecast Atmospheric Pollution
Pelliccioni, A., & Poli, U. (2000) · Environmental Monitoring and Assessment · doi:10.1023/a:1006419504230 · Cited by 8
Verfahren des Data Mining
Beekmann, F., & Chamoni, P. (2006) · Springer eBooks · doi:10.1007/3-540-33752-0_13 · Cited by 8
Dynamic and stability comparison analysis of the high-speed turbocharger rotor system with and without thrust bearing via machine learning schemes
Mutra, R. R., Srinivas, J., Reddy, D. M., et al. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-04892-0 · Cited by 7
“After the extraction of the vibration response from the sys-tem, a three-layer perceptron neural network model trained with an error back propagation learning rule is employed to classify the vibration responses either as stable or unstable [36].”
Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score
Salkic, N. N., Jovanović, P., Barišić-Jaman, M., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14100981 · Cited by 7
Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19
Štěpánek, L., Habarta, F., Malá, I., Štěpánek, L., Nakládalová, M., Boriková, A., & Marek, L. (2023) · Mathematics · doi:10.3390/math11040819 · Cited by 7
Neuromorphic Computing between Reality and Future Needs
Ahmed, K. S., & Shereif, F. F. (2023) · Artificial intelligence · doi:10.5772/intechopen.110097 · Cited by 7
PTC-CapsNet: capsule network for papillary thyroid carcinoma pathological images classification
Han, B., Han, Y., Li, H., & Gao, X. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18985-4 · Cited by 7
Associative Memory Networks
Du, K.-L., & Swamy, M. N. S. (2013) · Springer eBooks · doi:10.1007/978-1-4471-5571-3_7 · Cited by 7
Multi-objective Optimization of an Injection Molding Process
Alvarado‐Iniesta, A., García-Alcaráz, J. L., del Valle-Carrasco, A., et al. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-44003-3_16 · Cited by 7
Day-ahead forecasting of wholesale electricity pricing using extreme learning machine
Tee, J. E. C., Teo, T. P., Logenthiran, T., et al. (2017) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2017.8228371 · Cited by 7
Analysis of Fatigue Damage Information Obtained from Acoustic Emission Data
Lumb, R., Wayne, S. F., & Qi, G. (2020) · Data-Enabled Discovery and Applications · doi:10.1007/s41688-020-0036-7 · Cited by 7
Particles multiplicity based on rapidity in Landau and artificial neural network (ANN) models
Habashy, D. M., El-Bakry, M. Y., Tawfik, A. N., Rahman, R. M. A., & Hanafy, M. (2021) · International Journal of Modern Physics A · doi:10.1142/s0217751x22500026 · Cited by 7
Assessment of the Use of Patient Vital Sign Data for Preventing Misidentification and Medical Errors
Maul, J., & Straub, J. (2022) · Healthcare · doi:10.3390/healthcare10122440 · Cited by 7
Three-dimensional inverse design of axial compressor stator blade using neural-networks and direct Navier–Stokes solver
Giassi, A., Pediroda, V., Poloni, C., et al. (2003) · Inverse problems in engineering · doi:10.1080/1068276031000147545 · Cited by 7
Position Control of a DC Motor Used in Solar Panels with Artificial Neural Network
Şahin, M., Bülbül, H. İ., & Çolak, İ. (2012) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2012.216 · Cited by 7
Intelligent Seismic-Acoustic System for Identifying the Area of the Focus of an Expected Earthquake
Aliev, T. A. O. (2017) · InTech eBooks · doi:10.5772/65403 · Cited by 7
Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation
Liu, J., Chou, H.-C., Chen, C.-H., et al. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/3039454 · Cited by 7
Comparison of Speed and Accuracy of Chosen Magnetometer Calibration Algorithms in the Presence of External Interference
Kliment, T., Lipovský, P., Draganová, K., et al. (2018) · New Trends in Signal Processing (NTSP) · doi:10.23919/ntsp.2018.8524105 · Cited by 7
Hybrid Kriging and multilayer perceptron neural network technique for coverage prediction in cellular networks
Mezhoud, N., Oussalah, M., Zaatri, A., et al. (2020) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2020.1805609 · Cited by 7
Taxonomical Associative Memory
Rendeiro, D., Sacramento, J., & Wichert, A. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9198-4 · Cited by 8
Recall Performance for Content-Addressable Memory Using Adiabatic Quantum Optimization
Schrock, J., McCaskey, A., Hamilton, K. E., Humble, T. S., & Imam, N. (2017) · Entropy · doi:10.3390/e19090500 · Cited by 9
Short Term Load Forecasting by Adaptive Neural Network
Li, H. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/449/1/012028 · Cited by 7
Design principles of adaptive cellular immunity for artificial immune systems
van den Berg, H. A. (2008) · Soft Computing · doi:10.1007/s00500-008-0380-2 · Cited by 7
Neurointerfaces
Widrow, B. C., & Lamego, M. M. (2002) · IEEE Transactions on Control Systems Technology · doi:10.1109/87.987067 · Cited by 7
An Approach to Autonomic Deployment Decision Making
Kusber, R., Haseloff, S., & David, K. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-92157-8_11 · Cited by 7
Stability in the self-organized evolution of networks
Jansen, T., & Theile, M. (2007) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1276958.1277147 · Cited by 7
High Frequency Market Making with Machine LearningPreprint
Dixon, M. F. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2868473 · Cited by 7
Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical SystemPreprint
Banda, P., & Teuscher, C. (2014) · Cited by 8
Low-Thrust, Multiple-Near-Earth-Asteroid Mission Design with Sample Return to Earth Using Machine Learning
Viavattene, G., & Ceriotti, M. (2022) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34959 · Cited by 6
Machine learning techniques in monitoring and controlling friction stir welding process: a critical review
Kulkarni, B., Tayde, S., Chapke, Y., et al. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07133-8 · Cited by 6
Artificial immune system algorithm with neural network approach for social media performance metrics
Mansor, M. A., Sathasivam, S., & Kasihmuddin, M. S. M. (2018) · AIP conference proceedings · doi:10.1063/1.5041603 · Cited by 6
Optimization of Artificial Neural Networks Based Models for Wave Height Prediction
Stăvărache, G., Ciortan, S., & Rusu, E. V. C. (2020) · E3S Web of Conferences · doi:10.1051/e3sconf/202017303007 · Cited by 6
Data‐Driven Cz–Si Scale‐Up under Conditions of Partial Similarity
Dropka, N., Böttcher, K., Chappa, G. K., et al. (2024) · Crystal Research and Technology · doi:10.1002/crat.202300342 · Cited by 6
Long gamma-ray burst light curves as the result of a common stochastic pulse–avalanche process
Bazzanini, L., Ferro, L., Guidorzi, C., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202450150 · Cited by 6
Neural network-based decision support system for pre-diagnosis of psychiatric disorders
Bouaiachi, Y., Khaldi, M., & Azmani, A. (2014) · doi:10.1109/cist.2014.7016602 · Cited by 6
Local scour characteristics downstream of diversion barrages
Ghosh, M. K., Kumar, G. V., & Sen, D. (2009) · Proceedings of the Institution of Civil Engineers - Water Management · doi:10.1680/wama.2009.162.5.309 · Cited by 6
Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India
De, S. S., De, B. K., Chattopadhyay, G., et al. (2010) · Acta Geophysica · doi:10.2478/s11600-010-0047-0 · Cited by 6
Reservoir Simulation Assisted History Matching: From Theory to Design
Shams, M. U. (2016) · SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition · doi:10.2118/182808-ms · Cited by 6
An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies
Dehkordi, A. N. V., Kamali‐Asl, A., Ewing, J. R., et al. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3682 · Cited by 6
An approach for selecting countermeasures against harmful information based on uncertainty management
Kotenko, I. V., Saenko, I. B., Parashchuk, I. B., et al. (2021) · Computer Science and Information Systems · doi:10.2298/csis210211057k · Cited by 6
Application of supervised machine learning for prediction of probabilistic transient stability
Shahzad, U. (2021) · Australian Journal of Electrical & Electronics Engineering · doi:10.1080/1448837x.2021.2013418 · Cited by 6
Electricity Consumption Forecast Based on Neural Networks
Uakhitova, A. (2022) · Mathematical Models and Computer Simulations · doi:10.1134/s2070048222050167 · Cited by 6
Face recognition using Gabor filters, PCA and neural networks
Slavkovic, M. S., Reljin, B. D., Gavrovska, A. M., et al. (2013) · International Conference on Systems, Signals and Image Processing · doi:10.1109/iwssip.2013.6623443 · Cited by 6
Comparing Heterogeneous and Homogeneous Flocking Strategies for the Ghost Team in the Game of Ms. Pac-Man
Liberatore, F., Mora, A. M., Castillo, P. Á., & Merelo, J. J. (2015) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2015.2425795 · Cited by 7
Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow
Elmazoghi, H. G., Karakale, V., & طاهر, ل. س. ب. (2016) · Journal of Hydroinformatics · doi:10.2166/hydro.2016.078 · Cited by 6
Neural Network-Based Approach for Identification of Meteorological Factors Affecting Regional Sea-Level Anomalies
Moghadam, F. M. (2016) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0001472 · Cited by 8
Wavelet Decomposition Based Automatic Sleep Stage Classification Using EEG
Crasto, N., & Upadhyay, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56148-6_45 · Cited by 5
A cetacean monitoring system that integrates citizen science and satellite imagery
Vukelić, M., Mancini, F., Vukelić, D., & Carere, C. (2017) · RENDICONTI LINCEI · doi:10.1007/s12210-017-0657-4 · Cited by 6
Determination of Flow Parameters of a Water Flow Around an AUV Body
Hoth, J., & Kowalczyk, W. (2019) · Robotics · doi:10.3390/robotics8010005 · Cited by 6
Machine Learning Models for Error Detection in Metagenomics and Polyploid Sequencing Data
Krachunov, M., Nisheva-Pavlova, M. M., & Vassilev, D. I. (2019) · Information · doi:10.3390/info10030110 · Cited by 6
Quantum Hopfield Neural Networks: A New Approach and Its Storage Capacity
Meinhardt, N., Neumann, N. M. P., & Phillipson, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50433-5_44 · Cited by 6
On stability and associative recall of memories in attractor neural networks
Sampath, S., & Srivastava, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238054 · Cited by 7
Multiple‐Output Artificial Neural Network to Estimate Solid Cycle Times in Conical Spouted Beds
Estiati, I., Atxutegi, A., Altzibar, H., et al. (2021) · Chemical Engineering & Technology · doi:10.1002/ceat.202000491 · Cited by 6
Machine Learning
Schuld, M., & Petruccione, F. (2018) · Quantum science and technology · doi:10.1007/978-3-319-96424-9_2 · Cited by 6
Learning Two-Input Linear and Nonlinear Analog Functions with a Simple Chemical System
Banda, P., & Teuscher, C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08123-6_2 · Cited by 6
Multi-task learning for multi-dimensional regression : application to luminescence sensing
Michelucci, U., & Venturini, F. (2019) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-18733 · Cited by 6
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
Gómez-Mayorga, M. E., Gallegos-Funes, F. J., de la Rosa, J., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05258-3_23 · Cited by 6
Modularity enhances the rate of evolution in a rugged fitness landscape
Park, J.-M., Chen, M., Wang, D., et al. (2015) · Physical Biology · doi:10.1088/1478-3975/12/2/025001 · Cited by 6
Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon PhiPreprint
Dixon, M. F., Klabjan, D., & Bang, J. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2627258 · Cited by 6
Bloch Oscillations: Inverse Problem
González, J. A., Hernández-Ortíz, S., López-Vázquez, C., & Raya, A. (2016) · Plasmonics · doi:10.1007/s11468-016-0477-x · Cited by 4
Characterization and Feature Ranking of Well Log Variables Using Data-Driven Algorithms for Total Organic Carbon Estimation of Organic-Rich Shales
Kadri, M. M., Ganguli, S. S., Sen, S., Hacini, M., & Kumar, P. (2023) · Energy & Fuels · doi:10.1021/acs.energyfuels.3c02877 · Cited by 6
Data Mining Model for Prediction Effect of Corrosion Inhibition
Jafari, H., & Jafari, Z. (2018) · Journal of Bio- and Tribo-Corrosion · doi:10.1007/s40735-018-0139-y · Cited by 5
A Discrete Neuro Sliding Mode Control with Adaptive Switching Gain for Uncertain Nonlinear Systems
Raja, B. M., Houda, R., Dehri, K., et al. (2018) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd.2018.8570562 · Cited by 5
Rainfall Prediction using Machine Learning Techniques – A Comparative Approach
Sivanantham, S., Kumar, P. H., Vardhan, S. N., et al. (2023) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais56108.2023.10073697 · Cited by 5
The use of artificial neural networks in the determination of soil grain composition
Sekuła, K., Karłowska-Pik, J., & Kmiecik, E. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02480-7 · Cited by 5
Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects
Asadi, A., Baise, L. G., Sanon, C., et al. (2023) · Remote Sensing · doi:10.3390/rs15194883 · Cited by 5
Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations
Belány, P., Hrabovský, P., & Florková, Z. (2024) · Energies · doi:10.3390/en17051073 · Cited by 5
Enhancing Block-Oriented System Identification Using Prior-Informed Volterra Kernels
Vijayan, P., Dreesen, P., Lataire, J., et al. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3575968 · Cited by 5
Real time system for image acquisition and pattern recognition using Boolean neural network
Mazare, A. G., Ionescu, L. M., Lita, A.-I. I., et al. (2015) · International Spring Seminar on Electronics Technology · doi:10.1109/isse.2015.7248008 · Cited by 5
Training Multi Layer Perceptron Network Using a Genetic Algorithm as a Global Optimizer
Maaranen, H., Miettinen, K., & Mäkelä, M. M. (2003) · Applied optimization · doi:10.1007/978-1-4757-4137-7_20 · Cited by 5
Some Afterthoughts on Hopfield Networks
Šı́ma, J., Orponen, P., & AnttiPoika, T. (1999) · Lecture notes in computer science · doi:10.1007/3-540-47849-3_34 · Cited by 5
Virtual speed sensor for DC motor using back-propagation artificial neural networks
Montesdeoca, J. C., Zambrano, J., Morales-Garcia, J. A., et al. (2014) · IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) · doi:10.1109/ropec.2014.7036283 · Cited by 5
Determination of the geometry of the RVE for cancellous bone by using the effective complex shear modulus
Klinge, S. (2012) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-012-0408-5 · Cited by 5
Neural network classifier of attacks in IP telephony
Šafařík, J., Vozn̆ák, M., Mehić, M., et al. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2050671 · Cited by 5
Unmanned air vehicle stabilization based on neural network regulator
Andropov, S., Guirik, A. V., Budko, M. Y., et al. (2016) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2016-16-5-796-800 · Cited by 5
Reproducing a Neural Question Answering Architecture Applied to the SQuAD Benchmark Dataset: Challenges and Lessons Learned
Dür, A., Rauber, A., & Peter Filzmoser, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-76941-7_8 · Cited by 5
Global optimization of spin Hamiltonians with gain-dissipative systems
Kalinin, K. P., & Berloff, N. G. (2018) · Scientific Reports · doi:10.1038/s41598-018-35416-1 · Cited by 8
ZigBee wireless smart plug network with RSSI multi‐lateration‐based proximity estimation and parallelised machine learning capabilities for demand response
Deese, A. S., & Daum, J. (2020) · IET Wireless Sensor Systems · doi:10.1049/iet-wss.2018.5047 · Cited by 5
Key Points’ Location in Infrared Images of the Human Body Based on Mscf-ResNet
Ge, S., & Rum, S. N. M. (2021) · Future Internet · doi:10.3390/fi14010015 · Cited by 5
Accelerating activation function in higher order logic programming
Kasihmuddin, M. S. M., & Sathasivam, S. (2016) · AIP conference proceedings · doi:10.1063/1.4954542 · Cited by 5
Initial Work on the Development of a Hardware-Based Gradient Descent Trained Expert System
Ahmed, F., & Straub, J. (2022) · Systems · doi:10.3390/systems10050160 · Cited by 5
Comparison Study and Result Analysis of Improved Back- Propagation Algorithms in Bangla Speech Recognition
Rahman, M. M., & Bhuiyan, M. A.-A. (2015) · International Journal of Applied Research on Information Technology and Computing · doi:10.5958/0975-8089.2015.00014.7 · Cited by 5
PRIOR FORECASTING OF EPILEPTIC SEIZURE AND LOCALIZATION OF EPILEPTOGENIC REGION
Sharma, A., Rai, J. K., & Tewari, R. P. (2017) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237217500120 · Cited by 5

Authors: Rojas, R.
Affiliations: Freie Universität Berlin (DE)
Corresponding author: Rojas, Raúl
ORCID: Rojas, Raúl
Venue: Springer eBooks
Publisher: Springer Berlin Heidelberg
Published: 1996
DOI: 10.1007/978-3-642-61068-4
OpenAlex: W4241395986
Cited by: 810
References: 0
Citations by year: 2026: 15 · 2025: 20 · 2024: 24 · 2023: 40 · 2022: 38 · 2021: 50 · 2020: 47 · 2019: 47 · 2018: 61 · 2017: 51 · 2016: 62 · 2015: 53 · 2014: 49 · 2013: 28 · 2012: 35
FWCI: 1.07
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: artificial neural networks, neural network theory, computational model, computing paradigms

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Neural Networks

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks