Neural Networks

Machine Learning Approach for Solar Wind Categorization

Li, H., Wang, C., Tu, C., et al. (2020) · Earth and Space Science · doi:10.1029/2019ea000997 · Cited by 24

Machine learning models applied to friction stir welding defect index using multiple joint configurations and alloys

Nadeau, F., Thériault, B., & Gagné, M.-O. (2020) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/1464420720917415 · Cited by 24

Forecast of Hourly Average Wind Speed Using ARMA Model with Discrete Probability Transformation

Lujano-Rojas, J. M., Bernal-Agustín, J. L., Dufo‐López, R., et al. (2011) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-21765-4_125 · Cited by 24

Defect Inspection of Flip Chip Solder Bumps Using an Ultrasonic Transducer

Su, L., Shi, T., Xu, Z., et al. (2013) · Sensors · doi:10.3390/s131216281 · Cited by 24

Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation

González, R. M., Torbaghan, S. S., Gibescu, M., & Cobben, S. (2016) · Energies · doi:10.3390/en9070547 · Cited by 24

Practicability study on the suitability of artificial, neural networks for the approximation of unknown steering torques

van Ende, K. T. R., Schaare, D., Kaste, J., et al. (2016) · Vehicle System Dynamics · doi:10.1080/00423114.2016.1202987 · Cited by 24

Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches

Sabegh, M. H. Z., Mohammadi, M. N. S., & Naderi, B. (2017) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-017-0645-1 · Cited by 27

Forecasting sales using neural networks

Thiesing, F. M., & Vornberger, O. (1997) · Lecture notes in computer science · doi:10.1007/3-540-62868-1_125 · Cited by 24

Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network

Pennington, R. S., den Broek, W. V., & Koch, C. T. (2014) · Physical Review B · doi:10.1103/physrevb.89.205409 · Cited by 24

Software fault prediction using neuro-fuzzy network and evolutionary learning approach

Chatterjee, S. K., Nigam, S., & Roy, A. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2437-y · Cited by 23

An artificial neural network approach for remaining useful life prediction of equipments subject to condition monitoring

Tian, Z. (2009) · International Conference on Reliability, Maintainability and Safety · doi:10.1109/icrms.2009.5270220 · Cited by 23

Neural network simulation of spring flow in karst environments

Paleologos, E. K., Skitzi, I., Katsifarakis, K. L., & Darivianakis, N. (2013) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-013-0717-y · Cited by 24

ARTIFICIAL NEURAL NETWORKS IN LIQUID-LIQUID TWO-PHASE FLOW

Shirley, R. A., Chakrabarti, D. P., & Das, G. (2012) · Chemical Engineering Communications · doi:10.1080/00986445.2012.682323 · Cited by 23

Predicting protein complex geometries with a neural network

Chae, M., Krull, F., Lorenzen, S., & Knapp, E. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22626 · Cited by 25

ANN-BASED DECISION MODEL FOR THE REUSE OF VACANT BUILDINGS IN URBAN AREAS

Hsu, Y.-H., & Juan, Y. (2016) · International Journal of Strategic Property Management · doi:10.3846/1648715x.2015.1101626 · Cited by 23

Time series forecasting with genetic programming

Graff, M., Escalante, H. J., Tellez, F. O., et al. (2016) · Natural Computing · doi:10.1007/s11047-015-9536-z · Cited by 22

Machine vision of traffic state estimation using fuzzy logic

Quiros, A. R. F., Bedruz, R. A., Uy, A. C., et al. (2016) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2016.7848397 · Cited by 22

Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

Dropka, N., Böttcher, K., & Holeňa, M. (2021) · Crystals · doi:10.3390/cryst11101218 · Cited by 22

Image Pre-Processing

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_2 · Cited by 26

Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning

Riley, K. S., Koner, S., Osorio, J. C., Yu, Y., Morgan, H., Udani, J. P., Sarles, S. A., & Arrieta, A. F. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200158 · Cited by 26

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka, Z., Szczepański, J., & Pręgowska, A. (2024) · Electronics · doi:10.3390/electronics13040746 · Cited by 29

A Machine Learning Approach to Analyzing the Relationship Between Temperatures and Multi-Proxy Tree-Ring Records

Jevšenak, J., Džeroski, S., Zavadlav, S., et al. (2018) · Tree-Ring Research · doi:10.3959/1536-1098-74.2.210 · Cited by 22

Applying Machine Learning to the Phenomenological Flow Stress Modeling of TNM-B1

Stendal, J. A., Bambach, M., Eisentraut, M., Sizova, I., & Weiß, S. (2019) · Metals · doi:10.3390/met9020220 · Cited by 22

ADAPTIVE ALGORITHMS FOR NEURAL NETWORK SUPERVISED LEARNING: A DETERMINISTIC OPTIMIZATION APPROACH

Magoulas, G. D., & Vrahatis, M. N. (2006) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127406015805 · Cited by 22

A feedforward neural network controlling the movement of a 3-DOF finger

Secco, E. L., & Magenes, G. (2002) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2002.803101 · Cited by 22

W pair production cross-section and W branching fractions in e+e− interactions at 189 GeV

Abreu, P., Adam, W., Adye, T., et al. (2000) · Physics Letters B · doi:10.1016/s0370-2693(00)00314-2 · Cited by 22

Fuzzy Risk-Based Maintenance Strategy with Safety Considerations for the Mining Industry

Tubis, A. A., Werbińska-Wojciechowska, S., Śliwiński, P., et al. (2022) · Sensors · doi:10.3390/s22020441 · Cited by 21

Feature Learning and Deep Learning Architecture Survey

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22

Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link

Ahuna, M., Afullo, T. J. O., & Alonge, A. A. (2017) · IEEE AFRICON · doi:10.1109/afrcon.2017.8095546 · Cited by 21

Synthesis of model of hardware realization of LIF-model of biological neuron on the basis of FPGA

Slepova, L. O., & Zhilenkov, A. (2018) · IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering · doi:10.1109/eiconrus.2018.8317256 · Cited by 23

Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing

Michelucci, U., & Venturini, F. (2019) · Applied Sciences · doi:10.3390/app9224748 · Cited by 21

Food Sources of Potassium in the Average Polish Diet

Górska-Warsewicz, H., Rejman, K., Laskowski, W., & Kowalcze, K. (2019) · Nutrients · doi:10.3390/nu11122905 · Cited by 21

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

Chattopadhyay, S., Jain, R., & Chattopadhyay, G. (2009) · Meteorological Applications · doi:10.1002/met.138 · Cited by 21

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

Suárez-Araujo, C. P., Báez, P. G., Cabrera-León, Y., Procházka, A., Rodríguez-Espinosa, N., Viadero, C. F., & Initiative, A. D. N. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5545297 · Cited by 19

A study on the estimations of ( n , t ) reaction cross-sections at 14.5 MeV by using artificial neural network

Özdoğan, H., Üncü, Y. A., Şekerci, M., & Kaplan, A. (2021) · Modern Physics Letters A · doi:10.1142/s0217732321501686 · Cited by 20

Computational Models for Affect Dynamics

Vanhasbroeck, N., Ariens, S., Tuerlinckx, F., & Loossens, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-82965-0_10 · Cited by 22

Prediction of Permeability Coefficient k in Sandy Soils Using ANN

Wrzesiński, G., & Markiewicz, A. (2022) · Sustainability · doi:10.3390/su14116736 · Cited by 20

Artificial Neural Networks

Pires, P. B., Santos, J. D., & Pereira, I. V. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch037 · Cited by 20

Agricultural Land Consumption in Periurban Areas: a Methodological Approach for Risk Assessment Using Artificial Neural Networks and Spatial Correlation in Northern Italy

Mazzocchi, C., Corsi, S., & Sali, G. (2015) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-015-9168-9 · Cited by 19

Adiabatic quantum optimization for associative memory recall

Seddiqi, H., & Humble, T. S. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00079 · Cited by 21

Crack detection in beam-like structures using a wavelet-based neural network

Nematollahi, M. A., Farid, M., HEMATIYAN, M. R., et al. (2011) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1177/0954410011421709 · Cited by 20

A high‐frequency trade execution model for supervised learning

Dixon, M. F. (2018) · High Frequency · doi:10.1002/hf2.10016 · Cited by 20

Prediction of Plastic Damage Index for Assessing Rib Pillar Stability in Underground Metal Mine Using Multi-variate Regression and Artificial Neural Network Techniques

Mohanto, S., & Deb, D. (2019) · Geotechnical and Geological Engineering · doi:10.1007/s10706-019-01065-y · Cited by 20

Formulation of the Effect of Different Alloying Elements on the Tensile Strength of the in situ Al-Mg2Si Composites

Kurt, H. İ., & Oduncuoğlu, M. (2015) · Metals · doi:10.3390/met5010371 · Cited by 20

Evaluation of a machine learning classifier for metamodels

Nguyen, P. T., Rocco, J. D., Iovino, L., Ruscio, D. D., & Pierantonio, A. (2021) · Software & Systems Modeling · doi:10.1007/s10270-021-00913-x · Cited by 19

Loss surface of XOR artificial neural networks

Mehta, D. B., Zhao, X., Bernal, E. A., & Wales, D. J. (2018) · Physical review. E · doi:10.1103/physreve.97.052307 · Cited by 20

Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals

Sharma, A., Rai, J. K., & Tewari, R. P. (2018) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2018.1464074 · Cited by 19

Fast forecasting of VGF crystal growth process by dynamic neural networks

Dropka, N., Holeňa, M., Ecklebe, S., Frank‐Rotsch, C., & Winkler, J. (2019) · Journal of Crystal Growth · doi:10.1016/j.jcrysgro.2019.05.022 · Cited by 18

Comparative study of partial least squares and neural network models of near-infrared spectroscopy for aging condition assessment of insulating paper

Li, Y., Zhang, Y., Zhang, W., et al. (2019) · Measurement Science and Technology · doi:10.1088/1361-6501/ab5f74 · Cited by 19

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network

Amor, N., Noman, M. T., & Petrů, M. (2021) · Polymers · doi:10.3390/polym13183104 · Cited by 20

Learning Forward Models for the Operational Space Control of Redundant Robots

Salaün, C., Padois, V., & Sigaud, O. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05181-4_8 · Cited by 19

Grey model for prediction of pore pressure change

Mohammed, M., Watanabe, K., & Takeuchi, S. (2009) · Environmental Earth Sciences · doi:10.1007/s12665-009-0287-y · Cited by 19

Tiered prediction models for port vessel emissions inventories

Cammin, P., Yu, J., & Voß, S. (2022) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-022-09468-5 · Cited by 18

An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data

Sajindra, H., Abekoon, T., Wimalasiri, E. M., et al. (2023) · AgriEngineering · doi:10.3390/agriengineering5040106 · Cited by 18

Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information

Asadi, A., Baise, L. G., Koch, M., et al. (2024) · Natural Hazards · doi:10.1007/s11069-023-06399-8 · Cited by 18

Information processing with large-scale optical integrated circuits

Kielpinski, D., Bose, R., Pelc, J. S., Vaerenbergh, T. V., Mendoza, G., Tezak, N., & Beausoleil, R. G. (2016) · IEEE International Conference on Rebooting Computing (ICRC) · doi:10.1109/icrc.2016.7738704 · Cited by 18

Use of artificial neural network for spatial rainfall analysis

Tsangaratos, P., Rozos, D., & ANDREAS, B. (2014) · Journal of Earth System Science · doi:10.1007/s12040-014-0417-0 · Cited by 18

MRI estimation of contrast agent concentration in tissue using a neural network approach

Bagher‐Ebadian, H., Nagaraja, T. N., Paudyal, R., Whitton, P. A., Panda, S., Fenstermacher, J. D., & Ewing, J. R. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21332 · Cited by 19

A fuzzy logic approach to influence maximization in social networks

Atif, Y., AlFalahi, K., Wangchuk, T., et al. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01286-2 · Cited by 18

Hot spot temperature prediction and operating parameter estimation of racks in data center using machine learning algorithms based on simulation data

Chen, X., Tu, R., Li, M., et al. (2023) · Building Simulation · doi:10.1007/s12273-023-1022-4 · Cited by 17

Models for forecasting the traffic flow within the city of Ljubljana

Petelin, G., Hribar, R., & Papa, G. (2023) · European Transport Research Review · doi:10.1186/s12544-023-00600-6 · Cited by 20

Detection of Osteoporosis from Percussion Responses Using an Electronic Stethoscope and Machine Learning

Scanlan, J., Li, F. F., Umnova, O., et al. (2018) · Bioengineering · doi:10.3390/bioengineering5040107 · Cited by 17

A Comparative Study of Classifiers in the Context of Papaya Disease Recognition

Habib, M. T., Majumder, A., Nandi, R. N., et al. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-13-7564-4_36 · Cited by 17

Satellite image fusion based on modified central force optimization

Talal, T. M., Attiya, G., Metwalli, M. R., et al. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08471-7 · Cited by 17

Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach

Chou, T., Anooz, S. B., Grüneberg, R., et al. (2021) · Crystals · doi:10.3390/cryst12010008 · Cited by 17

Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm

Cheng, C., Huang, Y.-H., & Chen, H. (2015) · Soft Computing · doi:10.1007/s00500-015-1749-7 · Cited by 17

Earthmoving trucks condition level prediction using neural networks

Marinelli, M., Lambropoulos, S., & Petroutsatou, K. (2014) · Journal of Quality in Maintenance Engineering · doi:10.1108/jqme-09-2012-0031 · Cited by 17

Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution

Pham, G. N., Lee, S., Kwon, O., et al. (2018) · Symmetry · doi:10.3390/sym10040090 · Cited by 17

Recognition of handwritten digits using structural information

Behnke, S., Pfister, M., & Rojas, R. (2002) · doi:10.1109/icnn.1997.613997 · Cited by 17

Fuzzy logic, genetic algorithms, and artificial neural networks applied to cognitive radio networks: A review

Alkhayyat, A. H., Abedi, F., Bagwari, A., et al. (2022) · International Journal of Distributed Sensor Networks · doi:10.1177/15501329221113508 · Cited by 16

An Artificial Intelligence (AI) Framework to Predict Operational Excellence: UAE Case Study

Hassan, R. R., Talib, M. W. A., Dweiri, F. T., & Román, J. J. (2024) · Applied Sciences · doi:10.3390/app14062569 · Cited by 16

Comparison of eigensensitivity and ANN based methods in model updating of an eight-story building

Kesavan, P., Kumar, A., & Thakkar, S. K. (2015) · Earthquake Engineering and Engineering Vibration · doi:10.1007/s11803-015-0036-z · Cited by 17

Tangent-space gradient optimization of tensor network for machine learning

Sun, Z.-Z., Ran, S.-J., & Su, G. (2020) · Physical review. E · doi:10.1103/physreve.102.012152 · Cited by 18

Success prediction of android applications in a novel repository using neural networks

Dehkordi, M. R., Seifzadeh, H., Beydoun, G., et al. (2020) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00154-3 · Cited by 16

Weather Data Mining using Artificial Neural Network

Ghosh, S., Nag, A., Biswas, D., et al. (2011) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics.2011.6069300 · Cited by 16

Multimodal behavior realization for embodied conversational agents

Čereković, A., & Pandzic, I. S. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0530-2 · Cited by 16

Automated Evaluation of Kikuchi Patterns by Means of Radon and Fast Fourier Transformation, and Verification by an Artificial Neural Network

Schwarzer, R. A., & Sukkau, J. (2003) · Advanced Engineering Materials · doi:10.1002/adem.200300374 · Cited by 16

Identification of a block-structured model with several sources of nonlinearity

Mulders, A. V., Vanbeylen, L., & Usevich, K. (2014) · European Control Conference (ECC) · doi:10.1109/ecc.2014.6862455 · Cited by 15

A virtual layer of measure based on soft sensors

Maniscalco, U., & Rizzo, R. (2016) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-016-0350-y · Cited by 15

A Boltzmann machine for high-resolution prestack seismic inversion

Phan, S. D. T., & Sen, M. K. (2019) · Interpretation · doi:10.1190/int-2018-0234.1 · Cited by 15

Artificial Neural Networks for Multiple NEA Rendezvous Missions with Continuous Thrust

Viavattene, G., & Ceriotti, M. (2021) · Journal of Spacecraft and Rockets · doi:10.2514/1.a34799 · Cited by 15

Meta-modeling for Manufacturing Processes

Auerbach, T., Beckers, M., Buchholz, G., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25489-5_20 · Cited by 15

Introduction to Neural Networks

Sher, G. I. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4463-3_2 · Cited by 15

Takens-inspired neuromorphic processor: A downsizing tool for random recurrent neural networks via feature extraction

Márquez, B. A., Suárez-Vargas, J. J., & Shastri, B. J. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033030 · Cited by 17

Selbstoptimierende Produktionssysteme

Auerbach, T., Bauhoff, F., Beckers, M., Behnen, D., Brecher, C., Brosze, T., Buchholz, G., Büscher, C., Corves, B. J., Eppelt, U., et al. (2011) · Springer eBooks · doi:10.1007/978-3-642-20693-1_6 · Cited by 17

Detection of microorganismic flows by linear and nonlinear optical methods and automatic correction of erroneous images artefacts and moving boundaries in image generating methods by a neuronumerical hybrid implementing the Taylor’s hypothesis as a priori knowledge

Petermeier, H., Kowalczyk, W., Delgado, A. R., et al. (2007) · Experiments in Fluids · doi:10.1007/s00348-007-0269-3 · Cited by 15

High-Temperature Solvent Injection for Heavy-Oil Recovery From Oil Sands: Determination of Optimal Application Conditions Through Genetic Algorithm

Leyva-Gomez, H., & Babadagli, T. (2016) · SPE Reservoir Evaluation & Engineering · doi:10.2118/183638-pa · Cited by 14

Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits

Bordoni, S., Stanev, D., Santantonio, T., & Giagu, S. (2023) · Particles · doi:10.3390/particles6010016 · Cited by 13

Quantum machine learning with glow for episodic tasks and decision games

Clausen, J., & Briegel, H. J. (2018) · Physical Review A · doi:10.1103/physreva.97.022303 · Cited by 13

MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution

Duda, D., Krętowski, M., Azzabou, N., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24369-6_21 · Cited by 14

A nonlinear principal component analysis to study archeometric data

Bitetto, A., Mangone, A., Mininni, R. M., et al. (2016) · Journal of Chemometrics · doi:10.1002/cem.2807 · Cited by 14

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Álvarez, D., Cerezo-Hernández, A., López-Muñíz, G., de Castro, T. Á., Albi, T. R., Hornero, R., & del Campo, F. (2017) · InTech eBooks · doi:10.5772/66570 · Cited by 13

IIoT Edge Analytics: Deploying Machine Learning at the Wellhead to Identify Rod Pump Failure

Boguslawski, B., Boujonnier, M., Bissuel-Beauvais, L., Saghir, F., & Sharma, R. (2018) · SPE Middle East Artificial Lift Conference and Exhibition (MEALC) · doi:10.2118/192513-ms · Cited by 18

Software Effort Estimation for Agile Software Development Using a Strategy Based on k-Nearest Neighbors Algorithm

Sánchez, E. R., Maceda, H. C., & Santacruz, E. F. V. (2022) · doi:10.1109/enc56672.2022.9882947 · Cited by 14

Characterization and Classification of Local Protein Surfaces Using Self-Organizing Map

Sael, L., & Kihara, D. (2010) · International Journal of Knowledge Discovery in Bioinformatics · doi:10.4018/jkdb.2010100203 · Cited by 14

Recognition of an obstacle in a flow using artificial neural networks

Carrillo, M., Que-Salinas, U., González, J. A., & López-Vázquez, C. (2017) · Physical review. E · doi:10.1103/physreve.96.023306 · Cited by 13

Prediction of AC conductivity for organic semiconductors based on artificial neural network ANN model

Mohamed, R. A. (2019) · Materials Research Express · doi:10.1088/2053-1591/ab250a · Cited by 14

An Enhanced Approach for Weather Forecasting Using Neural Network

Nayak, R., Patheja, P. S., & Waoo, A. A. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-81-322-0491-6_76 · Cited by 14

Thermographic crack detection and failure classification

Oswald-Tranta, B., Sorger, M., & O'Leary, P. L. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3455991 · Cited by 14

A Study of Artificial Neural Network Architectures for Othello Evaluation Functions

Binkley, K. J., Seehart, K., & Hagiwara, M. (2007) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.22.461 · Cited by 14

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