Neural Networks

Predicting Bankruptcy Using Neural Networks in the Current Financial Crisis: A Study of U.S. Commercial BanksPreprint

López‐Iturriaga, F. J., López‐de‐Foronda, Ó., & Pastor‐Sanz, I. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1716204 · Cited by 14

Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms

Štěpánek, L., Dlouhá, J., & Martinková, P. (2023) · Mathematics · doi:10.3390/math11194104 · Cited by 18

Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Bassi, A., Bodas, S. T., Hasan, S. S., et al. (2023) · Metals · doi:10.3390/met14010049 · Cited by 13

Physically Informed Deep Learning Technique for Estimating Blood Flow Parameters in Four-Vessel Junction after the Fontan Procedure

Isaev, A., Dobroserdova, T. K., Danilov, A. A., et al. (2024) · Computation · doi:10.3390/computation12030041 · Cited by 13

IMPROVING FORECASTING ACCURACY OF THE S&P500 INTRA-DAY PRICE DIRECTION USING BOTH WAVELET LOW AND HIGH FREQUENCY COEFFICIENTS

Lahmiri, S. (2014) · Fluctuation and Noise Letters · doi:10.1142/s0219477514500084 · Cited by 13

Single-channel dereverberation by feature mapping using cascade neural networks for robust distant speaker identification and speech recognition

Nugraha, A. A., Yamamoto, K., & Nakagawa, S. (2014) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/1687-4722-2014-13 · Cited by 13

Calibration of Magnetometer for Small Satellites Using Neural Network

Kliment, T., Praslička, D., Lipovský, P., et al. (2017) · Acta Physica Polonica A · doi:10.12693/aphyspola.131.1129 · Cited by 13

Artificial neural network assisted by first-principles calculations for predicting transformation temperatures in shape memory alloys

Minami, D., Uesugi, T., Takigawa, Y., et al. (2019) · International Journal of Modern Physics B · doi:10.1142/s0217979219500553 · Cited by 13

Operational Optimization of Polymerization Reactors with Computational Fluid Dynamics and Embedded Molecular Weight Distribution Using the Iterative Surrogate Model Method

Kong, J. Y., Eason, J. P., Chen, X., et al. (2020) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.0c00367 · Cited by 13

Angle of attack measurement using low-cost 3D printed five hole probe for UAV applications

Sankaralingam, L., & Ramprasadh, C. (2020) · Measurement · doi:10.1016/j.measurement.2020.108379 · Cited by 13

Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Rohrmanstorfer, S., Komarov, M. M., & Mödritscher, F. (2021) · Mathematics · doi:10.3390/math9060624 · Cited by 13

Calculations of GDR parameters for deformed nuclei using LogitBoost classifier and artificial neural network

Üncü, Y. A., Danışman, T., & Özdoğan, H. (2022) · Modern Physics Letters A · doi:10.1142/s0217732322500791 · Cited by 13

Travel Time Prediction Using Empirical Mode Decomposition and Gray Theory

Chen, H.-K., & Wu, C.-J. (2012) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2324-02 · Cited by 13

Learning parameter optimization of Multi-Layer Perceptron using Artificial Bee Colony, Genetic Algorithm and Particle Swarm Optimization

Taşkıran, Z. G. Ç., Çimen, S., & Yıldırım, T. (2015) · IEEE International Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami.2015.7061899 · Cited by 13

Application of logit model and self‐organizing maps (SOMs) for the prediction of financial crisis periods in US economy

Gıovanıs, E. (2010) · Journal of Financial Economic Policy · doi:10.1108/17576381011070184 · Cited by 13

Adaptive filters: stable but divergent

Rupp, M. (2015) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-015-0289-8 · Cited by 13

Prediction with Recurrent Neural Networks in Evolutionary Dynamic Optimization

Meier, A., & Krämer, O. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77538-8_56 · Cited by 13

Effects of Climate Change on Soil Erosion Risk Assessed by Clustering and Artificial Neural Network

Aslan, Z., Erdemir, G., Feoli, E., et al. (2018) · Pure and Applied Geophysics · doi:10.1007/s00024-018-2010-y · Cited by 13

Automatic analysis of auditory nerve electrically evoked compound action potential with an artificial neural network

Charasse, B., Van, H. T., Chanal, J. M., et al. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.03.004 · Cited by 13

Recognition of Handwritten ZIP Codes in a Real—World Non-Standard-Letter Sorting System

Pfister, M., Behnke, S., & Rojas, R. (2000) · Applied Intelligence · doi:10.1023/a:1008316121543 · Cited by 12

Locality sensitive batch feature extraction for high-dimensional data

Ding, J., Wen, C., Li, G., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.076 · Cited by 12

Mobile system with real time route learning using Hardware Artificial Neural Network

Mazare, A. G., Ionescu, L. M., Lita, A.-I. I., et al. (2015) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai.2015.7301250 · Cited by 12

A machine learning approach for efficient multi-dimensional integration

Yoon, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-98392-z · Cited by 12

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Herrera, F., & Lozano, M. (2009) · Intelligent systems reference library · doi:10.1007/978-3-642-01799-5_4 · Cited by 12

Establishment of Relationship between Pavement Surface Friction and Mixture Design Properties

Rajaei, M., Sefidmazgi, N. R., & Bahia, H. U. (2014) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2457-12 · Cited by 12

Development of correlations and artificial neural network models to predict second-degree burn time for thermal-protective fabrics

Udayraj, U., Talukdar, P., Das, A., et al. (2016) · Journal of the Textile Institute · doi:10.1080/00405000.2016.1163917 · Cited by 12

Precipitation downscaling using the artificial neural network BatNN and development of future rainfall intensity-duration-frequency curves

Kueh, S. M., & Kuok, K. K. (2016) · Climate Research · doi:10.3354/cr01383 · Cited by 12

Cloud detection and movement estimation based on sky camera images using neural networks and the Lucas-Kanade method

Tuominen, P., & Tuononen, M. (2017) · AIP conference proceedings · doi:10.1063/1.4984528 · Cited by 12

Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Salau, J., Haas, J. H., Junge, W., & Thaller, G. E. (2020) · Animals · doi:10.3390/ani11010050 · Cited by 13

Differentially private human activity recognition for smartphone users

Garain, A., Dawn, R., Singh, S., & Chowdhury, C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13185-4 · Cited by 12

Station Stopping of Freight Trains with Pneumatic Braking

Bai, Y., Mao, B., Ho, T.-K., et al. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/172549 · Cited by 12

Networks of width one are universal classifiers

Rojas, R. (2004) · doi:10.1109/ijcnn.2003.1224071 · Cited by 12

Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models

Chattopadhyay, G., & Chattopadhyay, S. (2008) · Meteorological Applications · doi:10.1002/met.97 · Cited by 12

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

Martínez–Llop, P. G., Bobi, J. D. D. S., Jiménez, Á. S., et al. (2021) · Sustainability · doi:10.3390/su132112265 · Cited by 11

An Artificial Intelligence Radio Propagation Model Based on Geographical Information

Zhang, H., Dong, J., Liu, X., et al. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2022.3215818 · Cited by 11

Applied machine learning-based models for determining the magnitude of pore pressure and minimum horizontal stress

Sanei, M., Ramezanzadeh, A., & Asgari, A. (2024) · Arabian Journal of Geosciences · doi:10.1007/s12517-024-11997-2 · Cited by 11

Decoupling static nonlinearities in a parallel Wiener-Hammerstein system: A first-order approach

Dreesen, P., Schoukens, M., Tiels, K., et al. (2015) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2015.7151404 · Cited by 11

Predicting the rheology of self-consolidating concrete under hot weather

Al-Khatib, M. I., & Martini, S. A. (2017) · Proceedings of the Institution of Civil Engineers - Construction Materials · doi:10.1680/jcoma.16.00055 · Cited by 11

Calculations of the heat source parameters on the basis of temperature fields with the use of ANN

Wróbel, J., & Kulawik, A. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3594-y · Cited by 11

Turing: Then, Now and Still Key

Greer, K. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-29694-9_3 · Cited by 15

70th Anniversary of Publication: Warren McCulloch & Walter Pitts - A Logical Calculus of the Ideas Immanent in Nervous Activity

Pospı́chal, J., & Kvasnička, V. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-10783-7_1 · Cited by 11

Neural networks in petroleum geology as interpretation tools

Malvić, T., Velić, J., Horváth, J., & Cvetković, M. (2010) · Central European Geology · doi:10.1556/ceugeol.53.2010.1.6 · Cited by 11

The impact of climate change on New Zealand's electricity demand

Chen, G., & Lie, T. T. (2010) · IEEE International Conference on Probabilistic Methods Applied to Power Systems · doi:10.1109/pmaps.2010.5528429 · Cited by 11

Inverse analysis for heterogeneous materials and its application to viscoelastic curing polymers

Klinge, S., & Steinmann, P. (2015) · Computational Mechanics · doi:10.1007/s00466-015-1126-5 · Cited by 11

Kohonen Self-organizing Feature Maps as a Means to Benchmark College and University Websites

Cooper, C., & Burns, A. (2007) · Journal of Science Education and Technology · doi:10.1007/s10956-007-9053-7 · Cited by 11

Offline flexible optimisation of feed and speed in computer numerical control machining of sculptured surfaces exploiting dedicated cutting force metamodels

Benardos, P., & Vosniakos, G.-C. (2013) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405413508946 · Cited by 11

Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network

Wrzesiński, G., Sulewska, M. J., & Lechowicz, Z. (2018) · Applied Sciences · doi:10.3390/app8050781 · Cited by 11

A Discrete Adaptive Second Order Neuro Sliding Mode Control For Uncertain Nonlinear System

Raja, B. M., Houda, R., Dehri, K., et al. (2019) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta.2019.8717283 · Cited by 11

New optimization methods for designing rain stations network using new neural network, election, and whale optimization algorithms by combining the Kriging method

Safavi, M., Siuki, A. K., & Hashemi, S. R. (2020) · Environmental Monitoring and Assessment · doi:10.1007/s10661-020-08726-z · Cited by 11

Using Genetic Artificial Neural Network to Model Dam Monitoring Data

Yinghua, W., & Xu, C. W. (2010) · doi:10.1109/iccms.2010.80 · Cited by 11

Ischemic heart disease detection using selected machine learning methods

Ciecholewski, M. (2012) · International Journal of Computer Mathematics · doi:10.1080/00207160.2012.742189 · Cited by 11

Feedforward Neural Networks for Spatial Interaction: Are They Trustworthy Forecasting Tools?

Thill, J., & Mozolin, M. (2000) · Advances in spatial science · doi:10.1007/978-3-642-59787-9_17 · Cited by 11

Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks

Reuter, M., Hilker, M., Noël, S., et al. (2025) · Atmospheric measurement techniques · doi:10.5194/amt-18-241-2025 · Cited by 10

Threshold-selecting strategy for best possible ground state detection with genetic algorithms

Lässig, J., & Hoffmann, K. H. (2009) · Physical Review E · doi:10.1103/physreve.79.046702 · Cited by 10

Application of Gibbs–Markov random field and Hopfield-type neural networks for detecting moving objects from video sequences captured by static camera

Subudhi, B. N., Ghosh, S., & Ghosh, A. K. (2014) · Soft Computing · doi:10.1007/s00500-014-1440-4 · Cited by 10

Combination of particle swarm optimization algorithm and artificial neural network to propose an efficient controller for vehicle handling in uncertain road conditions

Aalizadeh, B., & Asnafi, A. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2689-6 · Cited by 11

3-satisfiability logic programming approach for cardiovascular diseases diagnosis

Mansor, M. A., Sathasivam, S., & Kasihmuddin, M. S. M. (2018) · AIP conference proceedings · doi:10.1063/1.5041553 · Cited by 10

Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Menéndez-García, L. A., Lasheras, F. S., Nieto, P. J. G., et al. (2020) · Mathematics · doi:10.3390/math8122205 · Cited by 10

Intelligent data structures selection using neural networks

Czibula, G., Czibula, I. G., & Găceanu, R. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0468-3 · Cited by 10

Hierarchical Online Problem Classification for IT Support Services

Song, Y., Sailer, A., & Shaikh, H. (2011) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2011.3 · Cited by 10

Hopf bifurcation analysis of coupled two-neuron system with discrete and distributed delays

Karaoğlu, E., Yılmaz, E., & Merdan, H. (2016) · Nonlinear Dynamics · doi:10.1007/s11071-016-2742-0 · Cited by 10

Performance analysis of activation function in higher order logic programming

Mansor, M. A., & Sathasivam, S. (2016) · AIP conference proceedings · doi:10.1063/1.4954543 · Cited by 11

Simulation of Blood Flow in Microfluidic Devices for Analysing of Video from Real Experiments

Bachratý, H., Bachratá, K., Chovanec, M., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78723-7_24 · Cited by 10

Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks

Gallo, M., & Luca, G. D. (2018) · Sensors · doi:10.3390/s18082640 · Cited by 10

Synthesis of Controlled Parameters of Cyber-Physical-Social Systems for Monitoring of Security Incidents in Conditions of Uncertainty

Kotenko, I. V., & Parashchuk, I. B. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1069/1/012153 · Cited by 10

Development of Deep Learning Based Human-Centered Threat Assessment for Application to Automated Driving Vehicle

Shin, D., Kim, H.-G., Park, K.-M., & Yi, K. (2019) · Applied Sciences · doi:10.3390/app10010253 · Cited by 10

Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes

Yıldız, Ö., & Berilgen, M. (2019) · Turkish Journal of Civil Engineering · doi:10.18400/tekderg.492280 · Cited by 10

On the Analysis of “Simple” 2D Stochastic Cellular Automata

Regnault, D., Schabanel, N., & Thierry, É. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88282-4_41 · Cited by 10

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

de Oliveira, M. A. (2010) · Neural Computing and Applications · doi:10.1007/s00521-010-0410-8 · Cited by 10

A modular ridge randomized neural network with differential evolutionary distributor applied to the estimation of sea ice thickness

Mozaffari, A., Scott, K. A., Chenouri, S., & Azad, N. L. (2016) · Soft Computing · doi:10.1007/s00500-016-2074-5 · Cited by 10

The design of intelligent washing machine controller based on FIS & ANFIS

Kaler, S., & Gupta, R. (2017) · International Conference on Information, Communication, Instrumentation and Control (ICICIC) · doi:10.1109/icomicon.2017.8279163 · Cited by 10

Evaluation of the Deterioration of Ceramic Claddings by Application of Artificial Neural Networks

de Souza, J. S., Silva, A. R., de Brito, J., Dias, J. L. R., & Bauer, E. (2020) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001471 · Cited by 9

Optimizing MACD Parameters via Genetic Algorithms for Soybean Futures

Wiles, P. S., & Enke, D. L. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.157 · Cited by 10

A New Nonlinear Neural Network for Solving QP Problems

Yan, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-12436-0_39 · Cited by 10

Predicting user action from skin conductance

Laufer, L., & Németh, B. (2008) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1378773.1378829 · Cited by 10

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity

Moosavi, A. A., Nematollahi, M. A., & Omidifard, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310622 · Cited by 9

Associative Memory Networks

Du, K.-L., & Swamy, M. N. S. (2019) · Springer eBooks · doi:10.1007/978-1-4471-7452-3_8 · Cited by 9

Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill‐Posed Inverse Problems using Images and Numerical Data

Dropka, N., Holeňa, M., Thieme, C., et al. (2023) · Crystal Research and Technology · doi:10.1002/crat.202300125 · Cited by 9

Machine learning links seed composition, glucosinolates and viability of oilseed rape after 31 years of long-term storage

Nagel, M., Holstein, K., Willner, E., et al. (2018) · Seed Science Research · doi:10.1017/s0960258518000259 · Cited by 9

Learnability and robustness of shallow neural networks learned by a performance-driven BP and a variant of PSO for edge decision-making

He, H., Chen, M., Xu, G., et al. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06019-1 · Cited by 9

Subsurface temperature estimation in a geothermal field based on audio-frequency magnetotelluric data

Maryadi, M., & Mizunaga, H. (2021) · Exploration Geophysics · doi:10.1080/08123985.2021.1949945 · Cited by 9

Prediction of fluid oil and gas volumes of shales with a deep learning model and its application to the Bakken and Marcellus shales

Şen, Ş. (2022) · Scientific Reports · doi:10.1038/s41598-022-23406-3 · Cited by 10

Potential Energy Surfaces for the Dynamics of Elementary Gas-Surface Processes

Gamallo, P., Martin-Gondre, L., Sayós, R., Crespos, C., & Larrégaray, P. (2013) · Springer series in surface sciences · doi:10.1007/978-3-642-32955-5_2 · Cited by 10

Use of Neural Net Models to Forecast Atmospheric Pollution

Pelliccioni, A., & Poli, U. (2000) · Springer eBooks · doi:10.1007/978-94-010-0932-4_32 · Cited by 9

Recursive ant colony optimization: a new technique for the estimation of function parameters from geophysical field data

Gupta, D. K., Gupta, J. P., Arora, Y., & Shankar, U. (2012) · Near Surface Geophysics · doi:10.3997/1873-0604.2012062 · Cited by 9

Artificial Intelligence Tools

Quiza, R., López-Armas, O., & Davim, J. P. (2012) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-642-28085-6_3 · Cited by 9

Prediction Interval of Cumulative Number of Software Faults Using Multilayer Perceptron

Momotaz, B., & Dohi, T. (2015) · Studies in computational intelligence · doi:10.1007/978-3-319-26396-0_4 · Cited by 9

A data-driven QoT decision approach for multicast connections in metro optical networks

Panayiotou, T., Ellinas, G., & Chatzis, S. (2016) · International Conference on Optical Network Design and Modeling (ONDM) · doi:10.1109/ondm.2016.7494074 · Cited by 10

Activation function comparison in neural-symbolic integration

Mansor, M. A., & Sathasivam, S. (2016) · AIP conference proceedings · doi:10.1063/1.4954526 · Cited by 9

A novel system for fast and accurate decisions of gold-stock markets in the short-term prediction

El-Rashidy, M. A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05019-x · Cited by 9

Neural Network-Based PCA: An Application to Approximation of a Distributed Parameter System

Bartecki, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29347-4_1 · Cited by 9

Visual Quality Recognition of Nonwovens using Wavelet Texture Analysis and Robust Bayesian Neural Network

Liu, J., Zuo, B., Vroman, P., Rabénasolo, B., Zeng, X., & Bai, L. (2010) · Textile Research Journal · doi:10.1177/0040517509357650 · Cited by 10

A Comparison of Surrogate Models in the Framework of an MDO Tool for Wing Design

Paiva, R. M., Crawford, C. A., Suleman, A., & Carvalho, A. R. D. (2009) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.2009-2203 · Cited by 10

Optimal deployment model of cloudlets in mobile Cloud Computing

Alakbarov, R. G., Pashayev, F., & Alakbarov, O. R. (2017) · IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) · doi:10.1109/icccbda.2017.7951913 · Cited by 8

Formation of Indicators for Assessing Technical Reliability of Information Security Systems

Kotenko, I. V., & Parashchuk, I. B. (2018) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon.2018.8501650 · Cited by 8

Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications

Saltık, S., KIYIKCI, O., Akman, T., et al. (2025) · Materials · doi:10.3390/ma18112582 · Cited by 8

Control of autonomous robot using neural networks

Barton, A., & Volná, E. (2017) · AIP conference proceedings · doi:10.1063/1.4992224 · Cited by 8

Assessment of Gradient Descent Trained Rule-Fact Network Expert System Multi-Path Training Technique Performance

Straub, J. (2021) · Computers · doi:10.3390/computers10080103 · Cited by 8

Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

Jung, W. S., Kim, S. E., & Kim, Y. (2021) · Water · doi:10.3390/w13172392 · Cited by 8

Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness

Olanipekun, A. T., Mashinini, P. M., Owojaiye, O. A., et al. (2022) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp6050091 · Cited by 8

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Achieving Parameter of DBSCAN Based on Datagrid

Xu, Q. (2010)

PACWON: A parallelizing compiler for workstations on a network

Das, D. K., & Das, P. P. (1998) · Journal of Systems Architecture · doi:10.1016/s1383-7621(97)00078-7