Dr. Haralambos Sarimveis

Assistant Professor

School of Chemical Engineering

National Technical University of Athens

Athens, Greece

Tel #: +30-210-7723237

Fax #: +30-210-7723138

e-mail: hsarimv@central.ntua.gr

Education

Diploma, National Technical University of Athens, 1990

M.Sc., Texas A&M University, 1992

Ph. D., Texas A&M University, 1995

Research Interests:

Design, Simulation and Analysis of Linear and Nonlinear Automatic Control Systems: The research effort in this field focuses on the development of Model Predictive Control (MPC) methodologies, which in general require the formulation and the solution of an optimization problem in real time.

· Neural Networks: Neural networks are generic modeling structures that are used for the identification of nonlinear systems based on input-output data. Emphasis has been given on the development of training algorithms for the Radial Basis Function (RBF) neural network architecture.

Fuzzy Logic Systems: This successful technology for developing intelligent systems is used for the construction of dynamical models, the design of fuzzy controllers and for pattern recognition.

Evolutionary Algorithms: The development of this stochastic type of algorithms offers efficient alternatives for solving complex chemical engineering optimization problems, where traditional algorithms often fail due to the non-convex nature and the high dimensionality of the problems.

Production Planning and Inventory Control: Development of optimal decision systems based on the control theory, which resemble as close as possible the real production environment.

Quantitative Structure-Activity Relationships: Development of mathematical relationships linking chemical structure and pharmacological activity in a quantitative manner for a series of compounds. Emphasis is given in utilization of QSARs in drug design.

PUBLICATIONS

Refereed Journal Publications

I.1 Sarimveis, H., H. Genceli and M. Nikolaou. Rigorous design of robust Model Predictive Controllers for processes with more inputs than outputs. (Computers & Chemical Engineering, 20, 972, S1065-S1070, 1996).

I.2 Sarimveis, H., H. Genceli and M. Nikolaou, Design of robust non-square constrained Model Predictive Control. (AIChE Journal, 42, 9, 2582 – 2593, 1996).

I.3 Sarimveis H., “Training algorithms and learning abilities of three different types of neural networks”, (Systems Analysis Modeling Simulation, 38, 555-581, 2000).

I.4 Sarimveis, H. and Th. Retsina, “Tissue softness prediction using neural network methodologies”, (Pulp&Paper Canada, 102, 42-45, 2001).

I.5 Sarimveis, H., “Inferential neural network sensors for on-line prediction of tissue paper quality parameters”, (Chemical Engineering Communications, 188, 231-242, 2001).

I.6 Tsekouras, G, H. Sarimveis and G. Bafas, “A method for fuzzy system identification based on clustering analysis”, (Systems Analysis Modeling Simulation, 39,543-558, 2001).

I.7 Tsekouras, G, H. Sarimveis, C. Raptis and G. Bafas, “A fuzzy logic approach for system qualitative characteristics”, (Computers & Chemical Engineering, 26, 429-438, 2002).

I.8 Sarimveis, H., A. Alexandridis, G. Tsekouras and G. Bafas, “A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space”, (Industrial & Engineering Chemistry Research, 41, 751-759, 2002).

I.9 Alexandridis, A., Κ. Siettos, H. Sarimveis, A. Boudouvis and G. Bafas, “Modeling of nonlinear process dynamics using Kohonen’s neural networks, fuzzy systems and Chebyshev series”, (Computers & Chemical Engineering, 26(4-5), 479-486, 2002).

I.10 Korres, D, G. Anastopoulos, E. Lois, A. Alexandridis, H. Sarimveis, G. Bafas, “A neural network approach to the prediction of diesel fuel lubricity”, (Fuel, 81(10), 1243-1250, 2002).

I.11 Sarimveis, H., A. Angelou, Th. Retsina, G. Bafas, “Minimization of production cost through optimal selection of inventory levels and production rates in pulp and paper mills” (TAPPI Journal, 2(7) 13-18, 2003).

I.12 Sarimveis,H., A. Angelou, Th. Retsina, S. Rutherford, G. Bafas, “Optimal energy management in pulp and paper mills”, (Energy Conversion and Management, 44(10) 1707-1718, 2003).

I.13 Sarimveis,H., G. Bafas, “Fuzzy model predictive control of nonlinear processes using genetic algorithms”, (Fuzzy Sets and Systems, 139(1) 59-80, 2003).

I. 14 Alexandridis, A., H. Sarimveis, G. Bafas, “A new algorithm for online structure and parameter adaptation of RBF networks”, (Neural Networks, 16(7) 1003-1017, 2003).

I.15 Tsekouras, G., H. Sarimveis, G. Bafas, “A simple algorithm for training fuzzy systems using input-output data” (Advances in Engineering Software, 34(5) 247-259, 2003).

I.16 Sarimveis, H, A. Alexandridis, G. Bafas, “A fast training algorithm for RBF networks based on subtractive clustering” (Neurocomputing, 51 501-505, 2003).

I.17 Karonis D., E. Lois, S. Stournas, F. Zannikos, A. Alexandridis, H. Sarimveis, “A neural network approach for the correlation of exhaust emissions form a diesel engine with diesel fuel properties”, (Energy and Fuels, 17(5), 1259-1265, 2003).

I.18 Sarimveis H. A. Alexandridis, S. Mazarakis, G. Bafas, “A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms”, (Computers and Chemical Engineering, 28(1-2), 209-217, 2004).

I.19 Vakalis, D., H. Sarimveis, C. T. Kiranoudis, A. Alexandridis, G. Bafas, “A GIS based operational system for wildland fire crisis management. I. Mathematical modeling and simulation”, (Applied Mathematical Modelling, 28(4), 389-410, 2004).

I.20 Vakalis, D., H. Sarimveis, C. T. Kiranoudis, A. Alexandridis, G. Bafas, “A GIS based operational system for wildland fire crisis management. II. System architecture and case studies”, (Applied Mathematical Modelling, 28(4), 411-425, 2004).

I.21 Keramitsoglou, I., C. Kiranoudis, H. Sarimveis, N. Sifakis, “A Multidisciplinary decision support system for forest fire crisis management”, (Environmental Management, 33(2), 212-225, 2004).

I.22 Tsekouras G., H. Sarimveis, “A new approach for measuring the validity of the fuzzy c-means algorithm”, (Advances in Engineering Software, 35(8-9), 567-575, 2004).

I.23 Alexandridis, A., H. Sarimveis, G. Bafas, “Modeling and control of continuous digesters using the PLS methodology”, (Chemical Engineering Communications, 191(10), 1271-1284, 2004).

I.24 Sarimveis H. and A. Nikolakopoulos, “A line up evolutionary algorithm for solving nonlinear constrained optimization problems”, (Computers and Operations Research, 32(6), 1599-1514, 2005).

I.25 Tsekouras G., H. Sarimveis, E. Kavakli, G. Bafas “A hierarchical fuzzy-clustering approach to fuzzy modeling”, (Fuzzy Sets and Systems, 150(2), 245-266, 2005).

I.26 Alexandridis A., P. Patrinos, H. Sarimveis, G. Tsekouras, “A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models”, (Chemometrics and Intelligent Laboratory Systems, 75(2), 149-162, 2005).

Ι. 27 Afantitis Α., G. Melagraki, K. Makridima, A. Alexandridis, H. Sarimveis, O. Iglessi-Markopoulou, “Prediction of High Weight Polymers Glass Transition Temperature Using RBF Neural Networks” (ΤΗΕOCHEM: Journal of Molecular Structure, 716(1-3), 193-198, 2005).

I.28 Keramitsoglou I., H. Sarimveis, C. T. Kiranoudis, N. Sifakis, “Radial basis function neural networks classification using very high spatial resolution satellite imagery: An application to the habitat area of Lake Kerkini (Greece)” (International Journal of Remote Sensing, 26(9), 1861-1880, 2005)

I.29 Doganis, Ph., H. Sarimveis, D. Koufos, G. Bafas, “An MILP model for optimal scheduling of the lube production plant”, (Chemical Engineering Communications, 192, 1067-1084, 2005).

I.30 Alexandridis, Α., H. Sarimveis, “A nonlinear adaptive MPC framework based on self-correcting RBF network models”, (AICHE Journal, 51(9), 2495-2506, 2005).

I.31 G. Melagraki, Afantitis Α., H. Sarimveis, O. Iglessi-Markopoulou, C. T. Supuran, “QSAR study on para – substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices”, (Bioorganic & Medicinal Chemistry, 14(4), 1108-1114, 2006).

I.32 Sarimveis, H., P. Doganis, A. Alexandridis, “A classification technique based on radial basis function neural networks”, (Advances in Engineering Software, 37(4), 218-221, 2006).

I.33 Doganis Ph., A. Alexandridis, P. Patrinos, H. Sarimveis, “Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing”, (Journal of Food Engineering, 75(2), 196-204, 2006).

I.34 G. Melagraki, Afantitis Α., K. Makridima, H. Sarimveis, O. Iglessi-Markopoulou “Prediction of toxicity using a novel RBF neural network training methodology”, (Journal of Molecular Modeling, 12(3), 297-305, 2006).

I.35 Aggelogiannaki E., H. Sarimveis, “Multiobjective constrained MPC with simultaneous closed loop identification”, (International Journal of Adaptive Control and Signal Processing, 20(4), 145-173, 2006).

I.36 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, "Prediction of the Intrinsic Viscosity of Polymer – Solvent Combinations using a QSPR model", (Polymer, 47(9), 3240-3248, 2006).

I.37 Keramitsoglou I., H. Sarimveis, C. T. Kiranoudis, Ch. Kontoes, N. Sifakis, E. Fitoka, “The performance of pixel window algorithms in the classification of habitats using VHSR imagery”, (ISPRS Journal of Photogrammetry and Remote Sensing, 60(4), 225-238, 2006).

I.38 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, "Investigation of Substituent Effect of 1-(3,3-Diphenylpropyl)-Piperidinyl Phenylacetamides Amides on CCR5 Binding Affinity using QSAR and Virtual Screening Techniques", (Journal of Computer-Aided Molecular Design, 20, 83-95, 2006).

I.39 G. Melagraki, Afantitis Α., H. Sarimveis, O. Iglessi-Markopoulou, A. Alexandridis “A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri”, (Molecular Diversity , 10(2), 213-221, 2006).

I.40 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, " A Novel QSAR Model for Predicting Induction of Apoptosis by 4-Aryl-4H-chromenes", (Bioorganic and Medicinal Chemistry, 14, 6686-6694, 2006).

I.41 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, “A Novel Simple QSAR Model for the Prediction of anti-HIV Activity Using Multiple Linear Regression Analysis”, (Molecular Diversity , 10, 405-414, 2006).

I.42 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, "A Novel QSAR Model for Evaluating and Predicting the Inhibition Activity of Dipeptidyl Aspartyl Fluoromethylketones", (QSAR & Combinatorial Science, 10, 928-935, 2006).

I.43 Nikolakopoulos A., H. Sarimveis, “A Threshold Accepting Heuristic with intense Local Search for the Solution of Special Instances of the Traveling Salesman Problem”, (European Journal of Operations Research, 177(3), 1911-1929, 2007).

I.44 Melagraki G., A. Afantitis, H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, " A novel QSPR model to predict θ(lower critical solution temperature) in polymer solutions using molecular descriptors", (Journal of Molecular Modeling, 13(1), 55-64, 2007).

I.45 Ph. Doganis, H. Sarimveis, “Optimal scheduling in a yogurt production line based on mixed integer linear programming”, (Journal of Food Engineering, 80, 445-453, 2007).

I.46 Melagraki G., A. Afantitis, H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, "Optimization of Biaryl Piperidine and 4-Amino-2-biarylurea MCH1 Receptor Antagonists using QSAR Modeling, Classification Techniques and Virtual Screening", (Journal of Computer-Aided Molecular Design, 21(5), 251-267, 2007).

I.47 Aggelogiannaki E., H. Sarimveis, D. Koubogiannis, "Model Predictive Temperature Control in Long Ducts by means of a neural network approximation tool", (Applied Thermal Engineering, 27, 2363-2369, 2007).

I.48 E. Aggelogiannaki, H. Sarimveis, “A simulated annealing algorithm for prioritized multiobjective optimization – Implementation in an adaptive model predictive control configuration”, (IEEE Transactions on Systems, Man, and Cybernetics--Part B, 37(4), 902-915 2007).

I.49 Melagraki G., A. Afantitis, H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, " Identification of a series of novel derivatives as potent HCV inhibitors by a ligand – based virtual screening optimized procedure", (Bioorganic and Medicinal Chemistry, 15, 7237-7247, 2007).

I.50 Maglogiannis I., H. Sarimveis, C. T. Kiranoudis, H. A. Chatzioannou, N. Oikonomou, V. Aidinis, “Radial Basis Function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images”, (IEEE Transactions on Information Technology in Biomedicine, 12(1), 42-54, 2008).

I.51 Doganis Ph., H. Sarimveis, “Optimal production scheduling for dairy industries”, (Annals of Operations Research, 159(1), 315-331, 2008).

I.52 Nikolakopoulos A, H. Sarimveis, “A Metaheuristic Approach for the Sequencing by Hybridization Problem with Positive and Negative Errors”, (Engineering Applications of Artificial Intelligence, 21(2), 247-258, 2008).

I.53 H. Sarimveis, P. Patrinos, C. D. Tarantilis, C. T. Kiranoudis, “Dynamic modeling and control of supply chain systems: A review”, (Computers and Operations Research, 35, 3530-3561, 2008).

I.54 Aggelogiannaki E., H. Sarimveis, " Nonlinear Model Predictive Control for Distributed Parameter Systems using Data Driven Artificial Neural Network Models", (Computers & Chemical Engineering, 32, 1233-1245, 2008).

I.55 P. L. Zervas, H. Sarimveis, J. A. Palyvos, N. C. G. Markatos, “Prediction of Daily Total Solar Radiation on Horizontal Surfaces Based on Neural-Network Techniques”, (Renewable Energy, 33, 1796-1803, 2008).

I.56 A. Afantitis, Melagraki G., H. Sarimveis, P. A. Koutentis, J Markopoulos, O. Iglessi-Markopoulou, "Development and Evaluation of a QSPR Model for the Prediction of Diamagnetic Susceptibility”, (QSAR & Combinatorial Science, 27(4), 432-436, 2008).

I.57 P. L. Zervas, H. Sarimveis, J. A. Palyvos, N. C. G. Markatos, “Model-Based Optimal Control of a Hybrid Power Generation System consisting of Photovoltaic Arrays and Fuel Cells”, (Journal of Power Sources, 181, 327-338, 2008).

I.58 E. Aggelogiannaki, Ph. Doganis, H. Sarimveis, " An Adaptive Model Predictive Control configuration for Production- Inventory Systems", (International Journal of Production Economics, 114, 165-178, 2008).

I.59 Doganis Ph., E. Aggelogiannaki, H. Sarimveis, "A combined model predictive control and time series forecasting framework for production-inventory systems”, accepted, International Journal of Production Research, 2008.

I.60 E. Aggelogiannaki, H. Sarimveis, " Design of a Novel Adaptive Inventory Control System Based on the On-Line Identification of Lead Time”, accepted, International Journal of Production Economics, 2008.

I.61 A. Afantitis, Melagraki G., H. Sarimveis, O. Iglessi-Markopoulou, G. Kollias, "A novel QSAR model for predicting the inhibition of CXCR3 receptor by 4-N-aryl-[1,4] diazepane ureas”, accepted, European Journal of Medicinal Chemistry, 2008.

I.62 P. L. Zervas, A. Tatsis, H. Sarimveis, N. C. G. Markatos, “Development of a novel computational tool for optimizing the operation of fuel cells systems: Application for Phosphoric Acid Fuel Cells”, accepted, Journal of Power Sources, 2008.

Conference Proceedings Publications

II.1 Nikolaou, M., and H. Sarimveis, “Process modeling with recurrent neural networks”, ANNIE, 1991, St. Louis, MO.

II.2 Sarimveis, H., H. Genceli and M. Nikolaou, “Rigorous design of robust model predictive controllers for processes with more inputs than outputs”, Escape- 6, May 1996, Rhodes, Greece.

II.3 Sarimveis, H. and Th. Retsina, “Inferential sensors for on-Line monitoring of quality parameters in the pulp and paper industry”, AspenWorld 2000 Conference, 2000.

II.4 Alexandridis, A., Κ. Siettos, H. Sarimveis, A. Boudouvis and G. Bafas, “Modeling of nonlinear process dynamics using Kohonen’s neural Networks, fuzzy systems and Chebyshev series”, Escape- 11, 2001, Kolding, Denmark.

II.5 Sarimveis H. , A. Alexandridis, A.Angelou, and Th. Retsina “Artificial intelligence tools for the on-line prediction of quality properties in pulp and paper processes”, Paper Summit, 2002, Atlanta, GA.

II.6 Sarimveis H., A.Angelou, and Th. Retsina “Mill wide optimization based on mathematical programming techniques”, Paper Summit, 2002, Atlanta, GA.

II.7 Alexandridis A., H. Sarimveis, A.Angelou Th. Retsina and G. Bafas “A Model Predictive Control scheme for continuous pulp digesters based on the Partial Least Square (PLS) modeling algorithm ”, Control Systems 2002, Stockholm, Sweden.

II.8 Sarimveis H., A.Angelou Th. Retsina, A. Alexandridis and G. Bafas, “A mathematical programming approach for the optimum production planning in pulp and paper mill”, Control Systems 2002, Stockholm, Sweden.

II.9 Sarimveis,H., A. Alexandridis, S.Mazarakis and G. Bafas, “A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms”, ESCAPE-12, 2002, The Hague, Netherlands.

II.10 Sarimveis H., A. Alexandridis and G. Bafas, “Neural network model identification based on the subtractive clustering method”, IFAC World Congress, 2002, Barcelona, Spain.

II.11 Alexandridis A., H. Sarimveis, G. Bafas and Th. Retsina “A neural network approach for modeling and control of continuous digesters” TAPPI Fall Conference, 2002, San Diego, CA.

II.12 Alexandridis A., H. Sarimveis, G. Bafas, “Adaptive control of continuous pulp digesters based on radial basis function neural network models” ESCAPE 13, Lappeenranta, Finland, 2003.

II.13 Alexandridis A., H. Sarimveis, G. Bafas, “A new nonlinear adaptive model predictive control scheme based on RBF neural network models”, 3^rd Chemical Engineering Conference for Collabororative Research in Eastern Mediterranean, Thessaloniki, Greece, 2003.

II.14 Alexandridis A., H. Sarimveis, G. Bafas, “Modeling of continuous digesters using adaptive RBF neural network models”, 11th Mediterranean Conference on Control and Automation MED'03, Rhodes, Greece, 2003.

II.15 Keramitsoglou I., H. Sarimveis, C. T. Kiranoudis, N. Sifakis, “Ecosystem classification using artificial intelligence neural networks and very high spatial resolution satellite imagery”, Remote Sensing 2003, Barcelona, Spain, 2003.

II.16 Patrinos P, A. Alexandridis, A. Afantitis, H. Sarimveis and O. Igglesi-Markopoulou, “Development of nonlinear Quantitative Structure-Activity Relationships using RBF networks and evolutionary computing”, ESCAPE 14, Lisbon, Portugal, 2004.

II.17 Aggelogiannaki E., H. Sarimveis and G. Bafas, “Multiobjective constrained MPC with simultaneous closed loop identification for MIMO processes”, ESCAPE 14, Lisbon, Portugal, 2004.

II. 18 Patrinos P, H. Sarimveis, “An RBF based neuro-dynamic approach for the control of stochastic dynamic systems”, IFAC World Congress 2005, Prague, Czech Republic, 2005.

II. 19 Aggelogiannaki E., H. Sarimveis, A. Alexandridis, “A prioritized Multiobjective MPC configuration using adaptive RBF networks and evolutionary computation, IFAC World Congress 2005, Prague, Czech Republic, 2005.

II. 20 Doganis, Ph., H. Sarimveis, G. Bafas, “Optimal production scheduling for dairy industries based on a neural network sales forecasting model”, IMACS 2005, Paris, France, 2005

II. 21 P. Patrinos, H. Sarimveis, Th. Retsina, S. Rutherford, A. Alexandridis, “Neural network model-based paper machine marginal cost curves” Engineering, Pulping, Environmental TAPPI 2005 Conference, Philadelphia, PA, USA, 28-31/8/2005.

II. 22 Aggelogiannaki, E. H. Sarimveis “Model Predictive Control for Distributed Parameter Systems using RBF neural networks”, ICINCO 2005, Barcelona, Spain, 14-17/9/2005.

II. 23 Aggelogiannaki E., H. Sarimveis “Prioritized adaptive Model Predictive Control using evolutionary algorithms”, 5^th International Conference on Technology and Automation ICTA'05, Thessaloniki, Greece 15-16/10/2005.

II. 24 Nikolakopoulos A, H. Sarimveis, “A Heuristic approach to the Vehicle Routing Problem with Time Windows and Simultaneous Pick-up and Delivery”, Third international workshop on freight transportation and logistics- ODYSSEUS 2006, Altea, Spain, 23-26/5/2006.

II. 25 Aggelogiannaki E., H. Sarimveis, “Affine Radial Basis Function Neural Networks for the Robust Control of Hyperbolic Distributed Parameter Systems”, ICRA 2006 : "International Conference on Intelligent Control, Robotics and Automation”, Barcelona, Spain, 22-24/10/2006.

II. 26 Doganis Ph., Aggelogiannaki E., H. Sarimveis, “A model predictive control and time series forecasting framework for supply chain management”, ICRA 2006 : "International Conference on Intelligent Control, Robotics and Automation”, Barcelona, Spain, 22-24/10/2006.

II. 27 P. Patrinos, H. Sarimveis, “An explicit optimal control approach for mean-risk dynamic portfolio allocation”, ECC 2007, Kos Island, Greece, 2-6/07/2007.

Conference Presentations

III.1 Nikolaou, M., and H. Sarimveis,. “Input-output exact linearization of nonlinear dynamical systems modeled by Recurrent Neural Networks”, AIChE Annual Meeting, 1992, Los Angeles, CA.

III.2 Sarimveis, H., H. Genceli and M. Nikolaou,. “Design of robust non-square constrained model predictive control”, AIChE Annual Meeting, November 1995, Miami, FL.

III.3 Sarimveis, H., “Inferential neural network sensors for on-line prediction of tissue paper quality parameters”, AIChE Annual Meeting, 1999, Dallas, TX.

III.4 Sarimveis,H., A. Angelou, J. Thanassekos and Th. Retsina, “Mill wide optimization using mixed integer and linear programming techniques”, PAPTAC MIDWEST BRANCH, 54^th Annual Meeting, 2000, Quebec, Canada.

III.5 Sarimveis, H. and Th. Retsina, “Tissue softness prediction using neural network methodologies”, 86^th Annual Meeting of the Pulp and Paper Technical Association of Canada, 2000, Montral, Canada.

III.6 Sarimveis, H., A. Angelou, Th. Retsina “Optimization of the powerhouse operation in pulp and paper mills using mixed integer and linear programming techniques”, 51^st Canadian Chemical Engineering Conference, 2001, Halifax, Canada.

IΙI.7 Sarimveis,H., A. Alexandridis A., G. Bafas, J. Thanassekos and Th. Retsina, “Multi-period optimization methodology for planning and scheduling of pulp and paper mills”, AIChE Annual Meeting, 2001, Reno, NV.

ΙΙΙ.8 Melagraki, G., K. Makridima, A. Afantitis, H. Sarimveis and O. Igglesi-Markopoulou, “A novel QSTR model to predict toxicity of aromatic compounds based on the RBF neural network architecture”, The 11th International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (QSAR 2004), 2004, Liverpool, England.

ΙΙΙ.9 Makridima K., A. Afantitis, G. Melagraki, P. Patrinos, H. Sarimveis and O. Igglesi-Markopoulou, “Using the Radial Basis Function (RBF) neural network architecture to develop QSARs for the prediction of the toxicity of phenols in Tetrahymena pyriformis”, The 11th International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (QSAR 2004), 2004, Liverpool, England.

ΙΙΙ.10 Afantitis, A., G. Melagraki, K. Makridima, H. Sarimveis and O. Igglesi-Markopoulou, “A QSTR model for the prediction of paraffins and cycloalkanes boiling points using RBF neural networks and topological indices”, The 11th International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (QSAR 2004), 2004, Liverpool, England.

ΙΙΙ.11 Afantitis, A., G. Melagraki, K. Makridima, H. Sarimveis and O. Igglesi-Markopoulou, “A QSPR model for the prediction of polyacenes properties using RBF neural networks and topological indices”, The 11th International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (QSAR 2004), 2004, Liverpool, England.

ΙΙΙ.12 Vakalis, D., H. Sarimveis, C.T. Kiranoudis, A. Alexandridis, G. Bafas, “Modeling and Simulation of Wildfires based on Artificial Intelligence Techniques”, ICCMSE 2004, Athens, Greece.

ΙΙΙ.13 G. Melagraki, A. Afantitis, H. Sarimveis, O. Igglesi-Markopoulou, J. Markopoulos. Carbonic Anhydrase II Inhibitors: QSAR study using topological indices for a large set of sulfonamides. 6^th Medicinal Chemistry Conference: Medicinal Chemistry Drug Discovery and Design, March 10-12 2005.

ΙΙΙ.14 A. Afantitis, G. Melagraki, H. Sarimveis, O. Igglesi-Markopoulou, J. Markopoulos. A novel approach to build QSAR models for a large group of HEPT derivatives. 6^th Medicinal Chemistry Conference : Medicinal Chemistry Drug Discovery and Design, March 10-12 2005

ΙΙΙ.15 K,Makridima, A. Afantitis, G. Melagraki, H. Sarimveis, O. Igglesi-Markopoulou, A QSAR study on the inhibitory activity of set of compounds (1-phenylbenzimidazoles) against the platelet – derived growth factor receptor (PDGFR). 6^th Medicinal Chemistry Conference: Medicinal Chemistry Drug Discovery and Design, March 10-12 2005.

ΙΙΙ.16 I. Maglogiannis, C. Kiranoudis, H. Sarimveis, “Classification of microscopic images using radial basis function neural networks”, 2^nd IEEE International Conference on Computational Intelligence in Medicine and Healthcare, Lisbon, Portugal, 29/6-1/7/2005.

ΙΙΙ.17 G. Melagraki, A. Afantitis, H. Sarimveis, P.A Koutentis, O. Igglesi-Markopoulou and J. Markopoulos.” Density Functional Theory study of 3-acyl tetramic acids complexes with metal ions” 8^th FIGIPAS Meeting in Inorganic Chemistry, Athens, Greece, 6-9 July 2005.

ΙΙΙ.18 Ph. Doganis, H. Sarimveis, G. Bafas, “Optimal production scheduling for dairy industries based on a neural networks sales forecasting model”, MISTA 2005 The 2^nd Multidisciplinary International Conference on Scheduling: Theory and Applications, NY, NY, USA, 18-21 July 2005.

ΙΙΙ.19 Ph. Doganis, H. Sarimveis, G. Bafas, “On the Formulation of the DNA Sequencing by Hybridization problem as an Asymmetric Traveling Salesman problem”, MISTA 2005 The 2^nd Multidisciplinary International Conference on Scheduling: Theory and Applications, NY, NY, USA, 18-21 July 2005.

III. 20 A. Afantitis, G. Melagraki, H. Sarimveis, P.A Koutentis, J. Markopoulos, O. Igglessi-Markopoulou “Investigation of Substituent Effect of Thiazole and Oxodiazole Butanoic Acids as Potent α_νβ₃ receptor antagonists using QSAR and Virtual Screening Techqiques” International Symposium on Chemistry, Biology & Chemistry, 28/05 –01/06 2006 Paphos, Cyprus