Publications

Scroll down to see i) Journal papers, ii) Book Chapters iii) Conference/Workshop Papers, iv) Abstracts in Journals, v) Technical Reports, vi) Newsletter Articles, vii) Book Reviews,  and viii) Editorials

Journal Papers

  1. Faubel, C., Arvanitidis, A.I., Iskandar, L., Martinez-Molina, A., & Alamaniotis, M., “Comparative analysis of artificial intelligence models for real-time and future forecasting of environmental conditions: A wood-frame historic building case study,” Journal of Building Engineering, Elsevier, December 2024, pp. 1-30. In press
  2. Valdez, L., Alamaniotis, M., & Heifetz, A., “Identification of Distorted Gamma Signature Patterns using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network,” Nuclear Technology, Taylor & Francis, August 2024, pp. 1-30. 
  3. Alamaniotis, M., “Mode-Driven Explainable Artificial Intelligence Approach for Estimating Background Radiation Spectrum in a Measurement Applicable to Nuclear Security,” Annals of Nuclear Energy, Elsevier, August 2024, pp. 1-21.
  4. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., “Examining the Impact of Natural Ventilation versus Heat Recovery Ventilation Systems on Indoor Air Quality: A Tiny House Case Study,” Buildings, MDPI, June 2024, pp. 1802 (1-12).
  5. Arvanitidis, A.I., Alamaniotis, M., “Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems,” Energies, MDPI,  2024, pp. 1-31.
  6. Karaiskos, P., Martinez-Molina, A., Munian, Y, & Alamaniotis, M., “Indoor Air Quality Prediction Modeling for a Naturally Ventilated Fitness Building Using RNN-LSTM Artificial Neural Networks,” Smart and Sustainable Built Environment, 2024, pp. 1-20. (in press)
  7. Gharibshahi, E., & Alamaniotis, M., “Artificial Intelligence Detection System of Radioactive Nanocomposites in Liquid Filled Containers for Nuclear Security,” Nuclear Technology, Taylor and Francis, May 2024, vol. 210, pp. 868-883.
  8. Shahabinejad, H., Sudac, D., Alamaniotis, M., Nad, K., & Obhodas, J., “Precise Gamma-ray Stabilization Using Full Spectral Information,” Radiation Physics and Chemistry, Elsevier, February 2024, vol. 215 pp. 111337(1-6). 
  9. Alamaniotis, M., & Alexiou, M, “Fuzzy Leaky Bucket Approach for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones,” Electronics, MDPI, 2024, vol. 13(4), pp. 722(1-20). 
  10. Gu, S., & Alamaniotis, M., “Sequential Deployment of Mobile Radiation Sensor Network using Reinforcement Learning in Radioactive Source Search,” Nuclear Technology, Taylor and Francis, January 2024, vol. 210(1), pp. 100-111.
  11. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., “Indoor Air Quality Investigations in a Naturally Ventilated Cross-Training Sports Center. A Case Study,” Journal of Building Engineering, Elsevier, October 2023, vol. 77, pp. 107457(1-13). 
  12. Mathew, J., Shirsagar, R., Abidin, D.Z., Griffin, J., Kanarachos, S., James, J., Alamaniotis, M., & Fitzpatrick, M.E., “A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data,” Scientific Reports, Nature, June 2023, vol. 13, pp. 9448(1-15).
  13. Gonzalez, R., Aryasomyajula, V.A., Ayyagari, K.S., Gatsis, N., Alamaniotis, M., & Ahmed, S., “Modeling and Studying the Impact of Dynamic Reactive Current Limiting in Grid-Following Inverters for Distribution Network Protection,” Electric Power Systems Research, Elsevier, July 2023, vol. 224, pp. 109609(1-8).
  14. Arvanitidis, A.I., Agarwal, V., & Alamaniotis, M., “Nuclear-Driven Integrated Energy Systems: A State-of-the-Art Review,” Energies, MDPI, May 2023, vol. 16(11), pp. 4293(1-23).
  15. Gonzalez, R., Ahmed, S., Alamaniotis, M., “Implementing Very-Short-Term Forecasting of Residential Load Demand using a Deep Neural Network Architecture,” Energies, MDPI, April 2023, vol. 16(9), pp. 3636(1-16)
  16. Nichiforov, C., & Alamaniotis, M., “Learning Matrix Profile Method for Discord-Based Attribution of Complex Consumption Behavior,” Cogent Engineering, Taylor & Francis, vol. 10, April 2023, pp. 2199518(1-16).
  17. Akritidis, L., Alamaniotis, M., & Bozanis, P., “FLAGR: A Flexible High-Performance Library for Rank Aggregation” SoftwareX, Elsevier, vol. 21, February 2023, pp. 101319(1-6).
  18. Ayyagari, K.S., Gonzalez, R., Jin, Y., Alamaniotis, M., Ahmed, S., & Gatsis, N., “Learning CVaR-Optimal Reactive Power Control Policies in Distribution Systems Using Artificial Neural Networks,” Journal of Modern Power Systems and Clean Energy, SGEPRI, vol. 11(1), January 2023, pp. 201-211.
  19. Nichiforov, C., Martinez-Molina, A., & Alamaniotis, M., “An Intelligent Big Data Analytics Method for Two-Dimensional Non-Residential Building Energy Forecasting,” Intelligent Decision Technologies, IOS Press, 15(4), December 2022 pp. 1-9. Accepted
  20. Qiao, Y., Chen, S., Alinizzi, M., & Alamaniotis, M., Labi, S., “Investigating the efficacy of machine learning techniques for IRI estimation based on pavement distress type, density, and severity,” Journal of Infrastructure systems, ASCE, vol. 28(4), December 2022, pp 04022035(1-18).
  21. Dimitroulis, P., & Alamaniotis, M., “Multimodal Energy Management System for Residential Building Prosumers Utilizing Various Lifestyles,” Electric Power Systems Research, Elsevier, December 2022, 108737(1-24).
  22. Arvanitidis, A.I., Bargiotas, D., Kontogiannis, D., Fevgas, A., & Alamaniotis, M., “Optimized Data-Driven Models for Short-Term Electricity Price Forecasting based on Signal Decomposition and Clustering Techniques,” Energies, MDPI, vol. 15, October 2022, pp. (7929)1-25.
  23. Mowen, D., Munian, Y., & Alamaniotis, M., “Improving Road Safety during Nocturnal Hours by characterizing Animal Poses utilizing CNN-based Analysis of Thermal Images,” Sustainability, MDPI, vol. 14(19), October 2022, 12133(1-15). Editor’s Selection Article
  24. Akritidis, L., Alamaniotis, M., Fevgas, A., Tsompanopoulou, P., & Bozanis, P., “Improving Hierarchical Short Text Clustering through Dominant Feature Learning,” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 31 (5), August 2022, pp. (#2250034)1-24.
  25. Gharibshahi, E., & Alamaniotis, M., “Modeling and simulation of radioactive nanomaterials of Pb-U, Pb-Th, and Pb-Co in liquid containers for nuclear security applications,” Nuclear Science and Engineering, Taylor and Francis, vol. 196 (8), June 2022, pp. 1006-1019.
  26. Alamaniotis, M., “Smart Data Analytic Based Transform of Low-Count Gamma-Ray Spectra for Enhancing Isotope Detection using a Self-Learning Window Driven Relevance Vector Regression,” IEEE Transaction on Nuclear Science, vol. 69(6), June 2022, pp. 1357-1365.
  27. Gharibshahi, E., & Alamaniotis, M., “Simulation and Modeling of Optical Properties of U, Th, Pb, and Co Nanoparticles of Interest to Nuclear Security using Finite Element Analysis,” Nanomaterials, MDPI, vol. 12(10), May 2022, pp. 1710 (1-13).
  28. Munian, Y., Martinez-Molina, A., Miserlis, D., Hernandez, H., & Alamaniotis, M., “A HOG-CNN Based System for Detection of Wild Animals in Nocturnal Hours with Application to Automobiles,” Applied Artificial Intelligence, CRC Taylor, vol. 36(1), 2022, pp. (e2031825) 1978-2006.
  29. Dimitroulis, P., & Alamaniotis, M., “A Fuzzy Logic Energy Management System of On-Grid Electrical System for Residential Prosumers,” Electric Power Systems Research, Elsevier, vol. 2022, January 2022, pp. 107621(1-14).
  30. Munian, Y., Martinez-Molina, A., & Alamaniotis, M., “An AI Driven Arousal System to Alert and Avoid the Crepuscular Animal-based Vehicle Collision,” Intelligent Decision Technologies, IOS Press, 15(4), December 2021, pp. 707-720.
  31. Nichiforov, C., Martinez-Molina, A., & Alamaniotis, M., “An Intelligent Approach for Performing Energy Driven Identification of Buildings Utilizing Joint Electricity-Gas Patterns,” Energies – Special Issue on AI for Buildings, vol. 14(22), 2021, 7465(1-11).
  32. Le, V., Ramirez, J., & Alamaniotis, M., “Intelligent Room based Identification of Electricity Consumption with Ensemble Learning Method in Smart Energy,” Energies – Special Issue on Intelligent Energy Systems and Energy Policy, vol. 14(20), 2021, pp. 6717(1-13).
  33. Campos, B., & Alamaniotis, M., “Review of Internal Cyber Attacks in Nuclear Facilities and an Artificial Neural Network Model for Implementing Cyberforensics,” Nuclear Technology and Radiation Protection, Vinca Institute, vol. XXXVI(2), June 2021, pp. 128-138.
  34. Holbrook, L., & Alamaniotis, M., “Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices,” International Journal on Tools with Artificial Intelligence, World Scientific Publishing Company, June 2021, vol. 30(4), pp. 2150021(1-21).
  35. Fevgas, A., Akritidis, L., Alamaniotis, M., Tsompanopoulou, P., & Bozanis, P., “HyR-tree: A Spatial index for hybrid Flash/3DXPoint Storage,” Neural Computing and Applications – Special Issue on Information, Intelligence, Systems and Applications, Springer, 2021, pp. 1-13.
  36. Miserlis, D., Jafari, A., M. Davies, Guda, T. & Alamaniotis, M., “Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey,” American Journal of Biomedical Science and Research, 12(2), 2021, pp. 182-188.
  37. Khan, A.A, Berg, O., Alamaniotis, M., & Ahmed, S., “Intelligent Anomaly Identification in Cyber-Physical Inverter-based Systems,” Electric Power Systems Research, Elsevier, April 2021, vol. 193, pp. (107024)1-13.
  38. Lagari, L., Pantopoulou, S., Alamaniotis, M., & Tsoukalas, L., “A Library of Radionuclide γ-ray Profiles for the Identification of Unknown Sources,” Nuclear Technology, Taylor and Francis, 2021, pp. 10
  39. Martinez-Molina, A., & Alamaniotis, M., “Enhancing Historic Building Performance with the Use of Fuzzy Inference System to Control the Electric Cooling System,” Sustainability, MDPI, July 2020, vol. 12(14), pp. 58848(1-14). 
  40. Ebrahimi, N., Guda, T., Alamaniotis, M., Miserlis, D., & Jafari, A., “Design Optimization of a Novel Networked Electromagnetic Soft Actuators System Based on Branch and Bound Algorithm,” IEEE Access, June 2020, vol. 8, pp. 119324-119335.
  41. Alamaniotis, M., “Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids,” Frontiers in Artificial Intelligence – Fuzzy Systems, January 2020, vol. 3(1), pp. 14. 
  42. Alamaniotis, M., & Karagiannis, G., “Application of Fuzzy Multiplexing of Learning Gaussian Processes for the Interval Forecasting of Wind Speed,” IET Renewable Power Generation – Special Issue from Medpower 2018, January 2020, vol. 14 (1), pp. 100-109.
  43. Alamaniotis, M., & Gatsis, N., “Evolutionary Multiobjective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition,” Energies – Special Issue Selected Papers from Medpower 2018, MDPI, June 2019, vol. 12, pp. (2470) 1-18.
  44. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., “Enhancing Privacy in Smart Cities through Morphing of Anticipated Demand Utilizing Self-Elasticity and Genetic Algorithms,” Sustainable Cities and Society, Elsevier, vol. 46, April 2019, pp. (101426)1-12.
  45. Mathew, J., Griffin, J., Alamaniotis, M., Kanarachos, S., & Fitzpatrick, M., “Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems,” Applied Soft Computing Journal, Elsevier, vol. 70, September 2019, pp. 131-146.
  46. Alamaniotis, M., Mathew, J., Chroneos, A., Fitzpatrick, M., & Tsoukalas, L.H., “Probabilistic Kernel Machines for Predictive Monitoring of Weld Residual Stress in Energy Systems,” Engineering Applications of Artificial Intelligence, Elsevier, vol. 71, May 2018, pp. 138-154.
  47. Mathew, J., Parfitt, D., Wilford, K., Riddle, N., Alamaniotis, M., Chroneos, A., Fitzpatrick, M., “Reactor Pressure Vessel Embrittlement: Insights from Neural Network Modelling,” Journal of Nuclear Materials, Elsevier, vol. 502, April 2018, pp. 311-322.
  48. Alamaniotis, M., Gatsis, N., & Tsoukalas, L.H., “Virtual Budget: Integration of Electricity Load and Price Anticipation for Load Morphing in Price-Directed Energy Utilization,” Electric Power Systems Research, Elsevier, vol. 158, May 2018, pp. 284-296.
  49. Alamaniotis, M., & Cappelli, M., “Intelligent Identification of Boiling Water Reactor State Utilizing Relevance Vector Machines,” ASME Journal of Nuclear Engineering and Radiation Science, American Society of Mechanical Engineers, vol. 4, April 2018, pp. (020904)1-9.
  50. Alamaniotis, M., & Karagiannis, G., “Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 5(3), pp. 1-14.
  51. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “Extending the K-means Clustering Algorithm to improve the Compactness of the Clusters,” Journal of Pattern Recognition Research, vol. 11(1), pp. 61-73, 2016.
  52. Fainti, R., Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “Hierarchical Method based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 4(4), October 2016, pp. 20-32.
  53. Lagari, P.L., Sobes, V., Alamaniotis, M., & Tsoukalas, L.H., “Application of Artificial Neural Networks for Reliable Nuclear Data for Nonproliferation Modeling and Simulation,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 4(4), October 2016, pp. 54-64.
  54. Alamaniotis, M., & Tsoukalas, L.H., “Fusion of Gaussian Process Kernel Regressors for Fault Prediction in Intelligent Energy Systems,” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 25 (4), August 2016, pp. (#1650023)1-17.
  55. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., “Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, vol. 4(3), July 2016, pp. 1-20.
  56. Lagari, L., Nasiakou, A., & Alamaniotis, M., “Evaluation of Human Machine Interface (HMI) on a Digital and Analog Control Room in Nuclear Power Plants Using a Fuzzy Logic Approach,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, 2016, vol. 4(2), April 2016, pp. 50-68.
  57. Alamaniotis, M., Bargiotas, D., & Tsoukalas, L.H., “Towards Smart Energy Systems: Application of Kernel Machine Regression for Medium Term Electricity Load Forecasting,” SpringerPlus – Engineering Section, Springer, vol. 5 (1), January 2016, pp. 1-15.
  58. Eklund, M., Alamaniotis, M., Hernandez, H., & Jevremovic, T., “Method of Characteristics – A Review with Application to Science and Nuclear Engineering Computation,” Progress in Nuclear Energy, Elsevier, vol. 85, November 2015, pp. 548-567.
  59. Chrysikou, V., Alamaniotis, M., & Tsoukalas, L.H., “A Review of Incentive based Demand Response Methods in Smart Electricity Grids,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global Publications, October 2015, pp. 62-73.
  60. Alamaniotis, M., Bargiotas, D., Bourbakis, N., & Tsoukalas, L.H., “Genetic Optimal Regression of Relevance Vector Machines for Electricity Price Forecasting in Smart Grids,” IEEE Transactions on Smart Grid, Institute of Electrical and Electronic Engineers, vol. 6(6), November 2015, pp. 2997-3005.
  61. Alamaniotis, M., Lee, S., & Jevremovic, T., “Intelligent Analysis of Low Count Scintillation Spectra using Support Vector Regression and Fuzzy Logic,” Nuclear Technology, American Nuclear Society, vol. 191 (1), July 2015, pp. 41-57.
  62. Alamaniotis, M., & Jevremovic, T., “Hybrid Fuzzy-Genetic Approach Integrating Peak Identification and Spectrum Fitting for Complex Gamma-Ray Spectra Analysis,” IEEE Transactions on Nuclear Science, vol. 62(3), June 2015, pp. 1262-1277.
  63. Alamaniotis, M., Choi, C. & Tsoukalas, L.H., “Application of Fireworks Algorithm in Gamma-Ray Spectrum Fitting for Radioisotope Identification,” International Journal of Swarm Intelligence Research – Special Issue on Developments and Applications of Fireworks Algorithm, IGI Global Publications, vol. 6 (2), April-June 2015, pp. 102-125.
  64. Bourbakis, N., Tsoukalas, L.H., Alamaniotis, M., Gao, R., & Kerkman, K., “DEMOS: A Distributed Model based on Autonomous, Intelligent Agents with Monitoring and Anticipatory Responses for Energy Management in Smart Cities,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global Publications, vol. 2(4), October-December 2014, pp. 80-98.
  65. Alamaniotis, M., Grelle, A., & Tsoukalas L., “Regression to Fuzziness Method for Estimation of Remaining Useful Life in Power Plant Components,” Mechanical Systems and Signal Processing, Elsevier, vol. 48 (1-2), October 2014, pp. 188-198.
  66. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L., “Creep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global Publications, vol. 2(2), April-June 2014, pp. 1-25.
  67. Alamaniotis, M., & Agarwal, V., “Fuzzy Integration of Support Vector Regressor Models for Anticipatory Control of Complex Energy Systems,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global Publications, vol. 2(2), April-June 2014, pp.26-40.
  68. Alamaniotis, M., Young, J., & Tsoukalas, L.H., “Assessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, vol. 2(1), January-March 2014, pp.1-10. [Also selected and included by Publisher in the edited Book “Research Methods: Concepts, Methodologies, Tools and Applications” as Chapter 46].
  69. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H, “Fuzzy-Logic Radioisotope Identifier for Gamma Spectroscopy in Source Search,” IEEE Transactions on Nuclear Science, vol. 60 (4), August 2013, pp. 3014-3024.
  70. Alamaniotis, M., Mattingly, J., & Tsoukalas, L.H., “Pareto Optimal Gamma Spectroscopic Radionuclide Identification using Evolutionary Computing,” IEEE Transactions on Nuclear Science, vol. 60 (3), June 2013, pp. 2222-2231.
  71. Alamaniotis, M., Mattingly, J., & Tsoukalas, L.H., “Kernel-based Machine Learning for Background Estimation of NaI Low Count Gamma Ray Spectra,” IEEE Transactions on Nuclear Science, vol. 60 (3), June 2013, pp. 2209-2221.
  72. Alamaniotis, M., & Tsoukalas, L.H., “Neuro-SVM Anticipatory System for Online Monitoring of Radiation and Abrupt Change Detection,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, vol. 1 (2), April-June 2013, pp. 40-53.
  73. Ikonomopoulos, A., Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., “Gaussian Processes for State Identification in Pressurized Water Reactors,” Nuclear Technology, American Nuclear Society, vol. 182(1), April 2013, pp. 1-12.
  74. McCoy, K., Alamaniotis, M., & Jevremovic, T., “A Conceptual Model for Integrative Monitoring of Nuclear Power Plants Operational Activities based on Historical Nuclear Incidents and Accidents,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, vol. 1 (1), January-March 2013, pp. 69-81.
  75. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Optimal Assembly of Support Vector Regressors with Application to System Monitoring,” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 27 (6), December 2012, pp. 1250034(1-17).
  76. Chatzidakis, S., Ikonomopoulos, A., & Alamaniotis, M., “An Algorithmic Approach for RELAP5/MOD3 Reactivity Insertion Analysis in Research Reactors,” Nuclear Technology, American Nuclear Society, vol. 179 (3), September 2012, pp. 392-406.
  77. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Evolutionary Multiobjective Optimization of Kernel-based Very Short-Term Load Forecasting,” IEEE Transactions on Power Systems, vol. 27 (3), August 2012, pp. 1477-1484.
  78. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Probabilistic Kernel Approach to Online Monitoring of Nuclear Power Plants,” Nuclear Technology, American Nuclear Society, vol. 177 (1), January 2012, pp.132-144.
  79. Alamaniotis, M., Ikonomopoulos, A., Jevremovic, T., & Tsoukalas, L.H., “Intelligent Recognition of Signature Patterns in NRF Spectra,” Nuclear Technology, American Nuclear Society, vol. 175 (2), August 2011, pp. 480-497.
  80. Alamaniotis, M., Terrill, S., Perry, J., Gao, R., Tsoukalas, L.H., & Jevremovic, T., “A Multisignal Detection of Hazardous Materials for Homeland Security,” Journal of Nuclear Technology and Radiation Protection, Vinca Institute, vol. 24 (1), April 2009, pp. 46-55.

Book Chapters

  1. Alamaniotis, M., “Modeling of Intelligent Control Systems in Nuclear Power Plants,” Handbook on Instrumentation and Control Systems for Nuclear Power Plants, Book edited by M. Cappelli, Elsevier, March 2023, Chapter 8, pp. 525-555.
  2. Alamaniotis, M., & Karagiannis, G., “Day Ahead Hourly Solar Power Forecasting using Relevance Vector Regression Models,” Fusion of Machine Learning Paradigms: Theory and Applications, Book Edited by. I.K. Hatzilygeroudis, G. Tsihrintzis, and L.C. Jain, Springer, February 2023, Chapter 6, pp. 119-127.
  3. Alamaniotis, M., “Intelligent Data Analytics for Reducing Electricity Consumption in Smart Cities,” Advances in Artificial Intelligence-based Technologies, Book Edited by. G. Tsihrintzis, M. Virvou, L. Tsoukalas, A. Esposito and L. Jain, Springer, 2022, Chapter 8, pp. 111-124.
  4. Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H., & Vavalis, E., “Dynamic Data Driven Partitioning of Smart Grid for Improving Power Efficiency by combining K-Means and Fuzzy Methods,” Handbook of Dynamic Data Driven Applications Systems, Book, edited by E. Blasch et al., 2nd Edition, Springer, May 2022, Chapter 22, pp. 513-535.
  5. Alamaniotis, M., Heifetz, A., “Survey of Machine Learning Methodologies in Radiation Data Analytics pertained to Nuclear Security,” Advances in Machine/Deep Learning-based Technologies, Book Edited by. G. Tsihrintzis, M. Virvou, L. Tsoukalas, A. Esposito and L. Jain, vol. 23, Springer, 2022, Chapter 6, pp. 97-115.
  6. Alamaniotis, M., “Neuro-Kernel-Machine Network Utilizing Deep Learning and its Application in Predictive Analytics for Smart Cities,” Advances in Data Science: Methodologies and Applications, Book edited by Gloria Phillips-Wren, Anna Esposito and Lakhmi C Jain, Springer: Berlin, 2020, Chapter 14, pp. 293-307.
  7. Alamaniotis, M., & Ktistakis-Papadakis, I., “Neurofuzzy Approach for Control of Smart Appliance for Implementing Demand Response in Price Directed Energy Utilization,” Artificial Intelligence Techniques for a Scalable Energy Transition, Book edited by M. Sayed-Mouchaweh, Springer: Berlin, 2020, Chapter 10, 2020, pp. 261-278.
  8. Holbrook, L., & Alamaniotis, M., “A Good Defense is a DNN: Defending the IoT with Deep Neural Networks,” Machine Learning Paradigms – Advances in Theory and Applications of Deep Learning, Book edited by G. Tsihrintzis and L. Jain, Springer, 2020, Chapter 6, pp. 125-145.
  9. Alamaniotis, M., “Multi-Kernel Decomposition Paradigm Implementing the Learning from Loads Approach in Smart Power Systems,” Machine Learning Paradigms – Applications of Learning and Analytics in Intelligent Systems, Book edited by G. Tsihrintzis, M. Virvou, E. Sakkopoulos, and L. Jain, vol. 1, Springer: Berlin, 2019, pp. 131-148.
  10. Alamaniotis, M., “Data Interpretation and Algorithms,” Active Interrogation in Nuclear Security-Science, Technology, and Systems, Book edited by I. Jovanovic and A. Erickson, Springer Nature, 2018, pp. 249-278.
  11. Alamaniotis, M., & Tsoukalas, L.H., “Assessment of Gamma-Ray Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures,” Critical Developments and Applications of Swarm Intelligence, Book edited by Yuhui Shi, IGI-Global, Chapter 7, 2018, pp. 155-181.
  12. Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H., & Vavalis, E., “Dynamic Data Driven Partitioning of Smart Grid Using Learning Methods,” Selected Topics on Dynamic Data Driven Application Systems (DDDAS), Edited Book, Springer, 2018.
  13. Alamaniotis, M., & Tsoukalas, L.H., “Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems using Gaussian Processes and Fuzzy Inference,” Data Analytics and Decision Support for Cybersecurity – Trends, Methodologies and Applications, Book edited by I. Palomares, H.K. Kalutarage and Y. Huang, Springer, 2017, Chapter 8, pp. 223-241.
  14. Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., “Data Driven Monitoring of Complex Energy Systems: Gaussian Process Kernel Machines for Fault Identification with Application to Boiling Water Reactors,” Intelligent Computing Systems, Book edited by G. Tsihrintzis, M. Virvou, and L. Jain, Studies in Computational Intelligence, vol. 627, Springer: Berlin, February 2016, Chapter 8, pp. 177-188.
  15. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Swarm Intelligence Optimization: Applications of Particle Swarms in Industrial Engineering and Nuclear Power Plants,” in Computational Intelligence Systems in Industrial Engineering, Book edited by Cengiz Kahraman, Springer & Atlantis Press, Chapter 9, November 2012, pp. 181-202.

Conference/Workshop Papers

  1. Alamaniotis, M., “Matrix Profile driven Cross-Structure Modeling of Background Radiation Measurements Applied to Anomaly Detection in Nuclear Security,” American Nuclear Society Winter Conference and Expo, Washington D.C., USA, November 17-21, 2024, pp. 1-4. Accepted
  2. Alamaniotis, , “Utilizing Matrix Profile with the DDDAS Framework for Anomaly Detection in Nuclear Security,” Dynamic Data Driven Applications Systems-2024, New Brunswick, NJ, USA, November 6-8, 2024, pp. 1-8. Accepted
  3. Alamaniotis, M., & Fevgas, A., “Intelligent Management of Integrated Energy Systems in Remote Maritime Environments utilizing Fuzzy Modes,” Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018), Athens, Greece, November 3-6, 2024, pp. 1-6.
  4. Alamaniotis, M., “Intelligent Scheduling of Floating Nuclear Reactor Operation for Implementation of Distributed Smart Energy Systems in Remote Coastal Locations,” 36th IEEE International Conference on Tools with Artificial Intelligence, Herndon, VA, USA, October 28-30, 2024, pp. 1-8. Accepted
  5. Alamaniotis, M., “Fuzzy-empowered Decision Making Integrated with DDDAS-Matrix Profile Framework for Anomaly Detection in Radiation Measurements,” 36th IEEE International Conference on Tools with Artificial Intelligence, Herndon, VA, USA, October 28-30, 2024, pp. 1-8. Accepted
  6. Chengu, S.A., Gatsis, N., Alamaniotis, M., Ahmed, S., “Hardware-in-the-Loop Testing of the Impact of Grid-Following Inverters Control on Momentary Cessation,” IEEE Energy Conversion Congress and Exposition, Phoenix, Arizona, USA, October 20-24, 2024, pp. 1-5. Accepted
  7. Arvanitidis, A.I, & Alamaniotis, M., “Optimal Economic Dispatch Scheduling in Competitive Energy Market Utilizing a Greedy Q-Learning Algorithm,” IEEE PES Innovative Smart Grid Technologies-Europe, October 14-17, 2024, pp. 1-5. Accepted
  8. Arvanitidis, A.I, Faubel, C., Martinez-Molina, A., & Alamaniotis, M., “Comparative Analysis of Artificial Intelligence Models for HVAC System Optimization in UNESCO Heritage Buildings,” 15thInternational Conference on Information, Systems and Applications (IISA 2024), Chania, Greece, July 2024, pp. 1-7.
  9. Squire, M., & Alamaniotis, M., “Intelligent Smoothing and Cumulative Sum Control Methods Applied to Signal Peak Detection in Gamma-Ray Measurements,” 15thInternational Conference on Information, Systems and Applications (IISA 2024), Chania, Greece, July 2024, pp. 1-7.
  10. Ramirez, J., Ahmed, S., & Alamaniotis, M., “Spot Price Prediction in Electricity Markets Using an Ensemble of Extreme Learning Machines,” 15thInternational Conference on Information, Systems and Applications (IISA 2024), Chania, Greece, July 2024, pp. 1-4.
  11. Arvanitidis, A.I, & Alamaniotis, M., “Q-Learning Empowered Economic Dispatch for Nuclear-Driven Integrated Energy Systems,” American Nuclear Society Annual Conference, June 17-21 2024, pp. 1-4.
  12. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., “Optimal insulation and comfort in tiny house design: Balancing energy efficiency and occupant comfort,” 37th PLEA conference, Wroclaw, Poland, June 25-28, 2024, pp.1-6. 
  13. Arvanitidis, A.I, & Alamaniotis, M., “Reinforcement Learning-Driven Decision-Making in Deregulated Electricity Markets Involving Greedy Agent-Based Participants,” Texas Power and Energy Conference, February 2024, pp. 1-6.
  14. Reyes, A.M., Chengu, A., Gatsis, N., Ahmed, S., & Alamaniotis, M., “Model Explainable AI Method for Fault Detection in Inverter-based Distribution Systems,” Texas Power and Energy Conference, February 2024, pp. 1-6.
  15. Chengu, A., Gatsis, N., Alamaniotis, M., & Ahmed, S., “A Novel Fault Ride-Through Scheme for Grid-Forming Inverters under Symmetrical and Asymmetrical Faults in Distribution Systems,” Texas Power and Energy Conference, February 2024, pp. 1-6.
  16. Alamaniotis, M., “Investigation of Computing a Background Radiation Model using self-processed Matrix Profile Method” 2023 ANS Winter Meeting and Technology Expo, Washington D.C., USA, November 12-15, November 2023, pp. 405-407. 
  17. Valdez, L., Alamaniotis, M., & Heifetz, A., “Anomaly Detection in Gamma Source Search Spectra with Hopfield Neural Network on Quantum Computer Simulator,” Advances in Nuclear Nonproliferation Technology and Policy Conference (ANTPC 2023), Washington D.C., USA, November 12-15, 2023, pp. 1. 
  18. Alamaniotis, M., “The Chameleon System: A new Approach in Anticipatory based Decision-Making Systems with Application to Electricity Markets,” 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Atlanta, GA, USA, November 2-4, 2023, pp. 1-8. 
  19. Akritidis, L., Fevgas, A., Alamaniotis, M., & Bozanis, P., “Conditional Data Synthesis with Deep Generative Models for Imbalanced Dataset Oversampling,” 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Atlanta, GA, USA, November 2-4, 2023, pp. 1-8. 
  20. Alamaniotis, M., “Explainable Prognostics Method through Differential Evolved Ensemble of Relevance Vector Machines,” Annual Conference of the PHM Society, Salt Lake City, UT, USA, October 28-November 2, 2023, pp. 1-6. 
  21. Le, V., Walton, C., & Alamaniotis, M., “Optimized FPGA based Cyber Threat Detection Algorithm for Nuclear Power Plants,” 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023),  Knoxville, TN, USA, July 15-21, 2023, pp. 1-8.
  22. Arvanitidis, A.I., Valdez, L., & Alamaniotis, M., “A Quantum Machine Learning Methodology for Precise Appliance Data Classification in Smart Grids,” 14thInternational Conference on Information, Systems and Applications (IISA 2023), Volos, Greece, July 2022, pp. 1-6.
  23. Munian, Y., Martinez-Molina, A., Alamaniotis, M., “Comparative Analysis of Thermogram and Preprocessed HoG Images using Machine Learning Classifiers,” 14thInternational Conference on Information, Systems and Applications (IISA 2023), Volos, Greece, July 2022, pp. 1-8.
  24. Squire, M., Alamaniotis, M., “Synergism of Fuzzy Numbers and Data Smoothing for Abrupt Change Detection in Gamma-Ray Measurements,” 14thInternational Conference on Information, Systems and Applications (IISA 2023), Volos, Greece, July 2022, pp. 1-6. 
  25. Gharibshahi, E., Miserlis, D., & Alamaniotis, M., “Investigation of Novel Muon Imaging System in Cardiovascular Operations: A simulation Approach,” 14thInternational Conference on Information, Systems and Applications (IISA 2023), Volos, Greece, July 2022, pp. 1-4. 
  26. Shahabinejad, H., Sudac, D., Alamaniotis, M., Nad, K., & Objodas, J., “Bulk Sample Analysis using associated alpha particle neutron generator and artificial neural network,” 14thInternational Conference on Information, Systems and Applications (IISA 2023), Volos, Greece, July 2022, pp. 1-4.
  27. Karaiskos, P., Martinez–Molina, A., & Alamaniotis, M., “Assessment of Particle Matter Exposure in a Naturally Ventilated Sport Facility and its Potential Impact on COVID-19 Transmission,” ARCC 2023 International Conference, Dallas, TX, USA, April 12-15, 2023, pp. 1-6.
  28. Arvanitidis, A.I., Alamaniotis, M., Kontogiannis, D., Vontzos, G., Laitsos, V., & Bargiotas, D., “Performance Analysis of Single and Multi-Step Short-Term Load Forecasts using Multilayer Perceptron,” The Thirteenth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies, Barcelona, Spain, March 13-17, 2023, pp. 1-5. Accepted
  29. Ayyagari, K.S., Munian, Y., Inupakutika, D., Butukuri, K., Gonzalez, R., & Alamaniotis, M., “Simultaneous Detection and Classification of Dust and Soil on Solar PhotoVoltaic Arrays Connected to a Large Scale Industry: a Case Study,” European Electricity Market Conference, Ljubljana, Slovenia, September 13-15, 2022, pp. 1-6.
  30. Gu, S., & Alamaniotis, M., “Radiation Sensor Placement using Reinforcement Learning in Nuclear Security Applications,” 13th International Conference on Information, Systems and Applications (IISA 2021), Corfu, Greece, July 2022, pp. 6.
  31. Gonzalez, R., Ahmed, S., & Alamaniotis, M., “Deep Neural Network Based Methodology for Very Short Term Load Residential Load Forecasting,” 13th International Conference on Information, Systems and Applications (IISA 2021), Corfu, Greece, July 2022, pp. 6.
  32. Miserlis, D., Munian, Y., Bohannon, W., Wechsler, M., Montero-Baker, M., Ferrer-Cardona, L., Davies, M., Koutakis, P., & Alamaniotis, M., “Convolutional Neural Network Analysis of Tissue Remodeling and Myopathy in Peripheral Arterial Disease,” 13th International Conference on Information, Systems and Applications (IISA 2021), Corfu, Greece, July 2022, pp. 8.
  33. Alamaniotis, M., “Multi-Kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making,” 14th International KES Conference – Intelligent Decision Technologies, Rhodes, Greece, June 20-22, 2022, Chapter 25, pp. 279-288.
  34. Squire, M., & Alamaniotis, M., “Fuzzy Logic Method for Trend Identification in Radiation Measurements taken with a Mobile Detector with Application to Nuclear Security,” Transaction of the American Nuclear Society Annual Meeting, Anaheim, CA, USA, June 12-16, 2022, vol. 126(1), pp. 562-565.
  35. Valdez, L., Alamaniotis, M., & Heifetz, A., “Classical and Quantum Hopfield Network for Application in Radiation Anomaly Detection,” American Nuclear Society Student Conference, Urbana-Champaign, IL, USA, April 14-16, 2022, pp. 1-4. BEST PAPER AWARD
  36. Ibukun, A., Martinez-Molina, A., Nnaji, C., Alamaniotis, M., & Sulbaran, T., “Utility of Wearable Sensing Devices for Environmental Monitoring on Construction Sites,” ASCE Construction Research Congress, Arlington, VA, USA, March 9-12, 2022, pp. 10.
  37. Gu, S., & Alamaniotis, M., “Radiation Sensor Placement using Model-Based Reinforcement Learning and Mutual Information,” ANS Winter Meeting and Technology Expo, November 30-December 4, 2021, Washington D.C., USA, pp. 3.
  38. Alamaniotis, M., & Heifetz, A., “An Explainable Artificial Intelligence Approach using a Hopfield Network in Nuclear Security Applications,” ANS Winter Meeting and Technology Expo, November 30-December 4, 2021, Washington D.C., USA, pp. 3.
  39. Akritidis, L., Alamaniotis, M., Fevgas, A., & Bozanis, P., “A Scalable Short-Text Clustering Algorithm Using Apache Spark,” 33rd IEEE International Conference on Tools with Artificial Intellingence, Virtual Conference, November 9-11, 2021, pp. 1-8.
  40. Nichiforov, C., & Alamaniotis, M., “Load-based Classification of Academic Buildings using Matrix Profile and Supervised Learning,” IEEE PES Innovative Smart Grid Technologies-Europe, October 18-21, 2021, Espoo, Finland, pp. 5.
  41. Gu, S., & Alamaniotis, M., “Irradiation-Driven Dynamic Path-Planning of Moving Airborne Solar Farms Using Reinforcement Learning,” IEEE PES Innovative Smart Grid Technologies-Europe, October 18-21, 2021, Espoo, Finland, pp. 5.
  42. Valdez, L., & Alamaniotis, M., “Isotope Recognition in Gamma Spectra by using an Image Driven Hopfield Neural Network,” 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, Virtual Conference, October 16-23, 2021, pp 2.
  43. Lawrence, J., & Alamaniotis, M., “Development of a Fuzzy Logic Representation Library of Radioisotopes with Application to Nuclear Security,” 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, Virtual Conference, October 16-23, 2021, pp 2.
  44. Munian, Y., Martinez-Molina, A., Alamaniotis, M., “Comparison of Image segmentation, HOG and CNN Techniques for the Animal Detection using Thermography Images in Automobile Applications,” 12th International Conference on Information, Systems and Applications (IISA 2021), Virtual Conference, July 2021, pp. 8.
  45. Alamaniotis, M., “Fuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security,” 9th International Workshop on Combinations of Intelligent Methods and Applications (in conjunction with IISA 2021), Virtual Conference, July 2021, pp. 8.
  46. Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G., “Data Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs,” IEEE PowerTech 2021, June 27 – July 2, 2021, Madrid, Spain, pp. 6. Accepted
  47. Alamaniotis, M., “iDoubleRad: Cyber-physical System for Enhancing Security of Radiation Measurements in IoT Connected Detectors based on Spectra Comparison using Fuzzy Theil-II Measure,” American Nuclear Society 2021 Annual Meeting, June 13-16, 2021, Providence, RI, USA, pp. 4.
  48. Sooby, E., Alamaniotis, M., & Heiftez, A., “Gaussian Process Ensemble for Corrosion Modeling and Prediction in Molten Salt Reactors,” 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021), June 13-16, 2021, Providence, RI, USA, pp. 8.
  49. Heifetz, A., Bakhtiari, S., Kultgen, D., Huang, X., Sanjie, J., & Alamaniotis, M., “Perspectives on Secure Communications with Advanced Reactors: Ultrasonic and Millimeter Waves Classical and Quantum Communications,” 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021), June 13-16, 2021, Providence, RI, USA, pp. 8. 
  50. Martinez-Molina, A., Williamson, K; I. Owolusi, & Alamaniotis, M., “The impact of different building assemblies on thermal and air quality performance. An experimental student project in an architectural course comparing city building code versus Passive house standard,” Building Technology Educators’ Society (BTES) Conference, June 10-12, 2021, Auburn, AL, USA, pp.1-8. 
  51. Munian, Y., Martinez-Molina, A., & Alamaniotis, M., “A Design and Implementation of a Nocturnal Animal Detection Intelligent System in Transportation Applications,” ASCE International Conference on Transportation & Development, June 6-9, 2021, Virtual Conference, pp. 12. Accepted
  52. Hooker, A., Alamaniotis, M., “K-Nearest Neighbor Approach to determine Data Injection Attack on Radiation Sensor in IoT based Security System,” American Nuclear Society 2021 Student Conference, April 8-10, 2021, Virtual Meeting, USA, pp. 4.
  53. Alamaniotis, M., and Heifetz, A., “A Machine Learning Approach for Background Radiation Modeling and Anomaly Detection in Radiation Time Series pertained to Nuclear Security,” Winter Meeting and Technology Expo, Chicago, IL, USA, November 15-19, 2020, pp. 477-480.
  54. Alamaniotis, M., “Intelligent Data Smoothing of Gamma-Ray Spectra using Relevance Vector Machines with Application to Nuclear Security,” Winter Meeting and Technology Expo, Chicago, IL, USA, November 15-19, 2020, pp. 481-483.
  55. Goodman, G., Hirt, Q., Shimizu, C., Papadakis-Ktistakis, I., Alamaniotis, M., & Bourbakis, N., “Methods for Prediction Optimization of the Constrained State-Preserved Extreme Learning Machine,” IEEE International Conference on Tools with Artificial Intelligence 2020, November 8-11, 2020, pp. 639-646.
  56. Akritidis, L., Alamaniotis, M., Fevgas, A., & Bozanis, P., “Confronting Sparseness and High Dimensionality in Short Term Clustering via Feature Vector Projections,” IEEE International Conference on Tools with Artificial Intelligence 2020, Virtual Conference, November 8-11, 2020, pp. 813-820.
  57. Miserlis, D., Jafari, A., Guda, T., & Alamaniotis, M., “Fuzzy Logic Navigation System for Autonomous Endovascular Operations,” 20th IEEE International Conference on Bioinformatics and Bioengineering, Virtual Conference, October 26-28, 2020, pp. 865-870.
  58. Munian, Y., Alamaniotis, M., Martinez-Molina, A., “Intelligent System for Detection of Wild Animals Using HOG and CNN in Automobile Applications,” 9th International Conference on Information, Systems and Applications (IISA), Piraeus, Greece, July 2020, pp. 8. 
  59. Alamaniotis, M., “Predicting Background Count Rate of a Mobile Detector using an optimal ensemble of Learning Kernel Machines,”American Nuclear Society Annual Meeting, Virtual Conference, June 7-11, 2020, pp.185-188.
  60. Dimitroulis, P., & Alamaniotis, M., “Residential Energy Management System utilizing Fuzzy Based Decision-Making,” IEEE Texas Power and Energy Conference, College Station, TX, USA, February 6-7, 2020, pp. 6. 
  61. Prakash, V., Fontenot, H., Khan, A., Bing, D., & Alamaniotis, M., “Ensemble Method for Short-Term Load Forecasting Using LSTM, SVR, and FNN and Taking into Account Seasonal Dependency,” ASHRAE Winter Conference and Expo, Orlando, FL, USA, February 1-5, 2020, pp. 1-8.
  62. Campos, B., & Alamaniotis, M., “Lessons Learned about Network Defenses of Nuclear Power Plants: A Critical Analysis of Internal Cyber-Attacks,” ANS Winter Meeting and Winter Expo, Washington D.C., November 17-21, 2019, vol. 121 (1), pp. 511-514.
  63. Alamaniotis, M., “A Data-Driven Methodology for Estimation of Background Spectrum Utilizing Paired Machine Learning Tools,” ANS Winter Meeting and Winter Expo, Washington D.C., November 17-21, 2019, vol 121 (1), pp. 578-581. 
  64. Holbrook, L., & Alamaniotis, M., “Internet of Things Security Analytics with Deep Learning” 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019), Portland, OR, USA, pp. 178-185. Best student paper Award
  65. Alamaniotis, M., “ELM-Fuzzy Method for Automated Decision Making in Price Directed Electricity Markets,” 16th International Conference on the European Energy Market, Ljubljana, Slovenia, September 18-20, 2019, pp. 5.
  66. Alamaniotis, M., “Synergism of Deep Neural Network and ELM for Very Short Term Load Forecasting,” Innovative Smart Grid Technologies Europe, Buchurest, Romania, September 29-October 2,2019, pp. 5.
  67. Ayyagari, S.K., Gonzalez, R., Jin, Υ., Alamaniotis, M., Ahmed, S., & Gatsis, N., “Artificial Neural Network-Based Voltage Regulation in Distribution Systems using Data-Driven Stochastic Optimization,” IEEE Energy Conversion Congress & Expo, Baltimore, MD, USA, September 29-October 3, 2019, pp. 5.
  68. Akritidis, L., Fevgas, A., P. Bozanis, & Alamaniotis, M., “A Self-Pruning Classification Model for News,” 10th International Conference on Information, Intelligence, Systems, and Applications, Patras, Greece, July 15-17, 2019, pp.6.
  69. Bhagat, M., Alamaniotis, M., Fevgas, A. “Extreme Interval Electricity Price Forecasting of Wholesale Markets Integrating ELM and Fuzzy Inference,” 10th International Conference on Information, Intelligence, Systems, and Applications, Patras, Greece, July 15-17, 2019, pp. 4.
  70. Fevgas, A., Akritidis, L., Alamaniotis, M., Tsompanopoulou, P., & Bozanis, P., “A Study of R-tree Performance in Hybrid Flash/3DXPoint Storage,” 10th International Conference on Information, Intelligence, Systems, and Applications, Patras, Greece, July 15-17, 2019, pp. 6.
  71. Alamaniotis, M., & Karagiannis, “Minute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Driven Assimilation,” IEEE PowerTech, Milano, Italy, June 23-27, 2019, pp. 6.
  72. Alamaniotis, M., “Fuzzy Data Fusion Utilizing Relevance Vector Machines with Application to Pressurized Water Reactor Monitoring,” 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Orlando, FL, USA, February 9-14, 2019, pp. 1-7.
  73. Alamaniotis, M., and Ray, A., “A Machine Learning Method Integrating Neural Networks and Learning Gaussian Processes for LOCA Identification in BWRs,” 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Orlando, FL, USA, February 9-14, 2019, pp. 1-8.
  74. Alamaniotis, M., & Karagiannis, G., “Learning Uncertainty of Wind Speed Forecasting using a Fuzzy Multiplexer of Gaussian Processes,” Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018), Dubrovnik, Croatia, November 12-15, 2018, pp. 1-6.
  75. Alamaniotis, M., & Gatsis, N., “Evolutionary Load Morphing in Smart Power System Partitions Ensuring Privacy and Minimizing Cost,” Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018), Dubrovnik, Croatia, November 12-15, 2018.
  76. Verney-Provatas, A., Alamaniotis, M., Choi C.K., & Tsoukalas, L.H., “A Simulation Platform for Data Generation in Analysis of Detection Algorithms in Radioactive Source Search,” American Nuclear Society Winter Meeting, Orlando, FL, USA, November 11-15, 2018, pp. 1-3.
  77. Alamaniotis, M., and Tsoukalas, L.H., “Peak Locating in Gamma-Ray Spectra Using Wavelet Processing and Support Vector Regression with Applications to Nuclear Nonproliferation,” Advances in Nuclear Nonproliferation and Policy Conference, Orlando, FL, USA, November 11-15, 2018, pp. 1-4.
  78. Alamaniotis, M., & Papadakis-Ktistakis, I., “Fuzzy Leaky Bucket with Application to Coordinating Smart Appliances in Smart Homes,” 30th IEEE International Conference on Tools with Artificial Intelligence, Volos, Greece, November 5-7, 2018, pp. 1-6.
  79. Alamaniotis, M., “Morphing to the Mean Approach of Anticipated Electricity Demand in Smart City Partitions using Citizen Elasticities,” IEEE International Smart Cities Conference, Kansas City, MS, USA, September 15-17, 2018, pp. 1-7.
  80. Fainti, R., Karasimou, M., Tsionas, I., Tsoukalas, L.H. & Alamaniotis, M., “Load Management of Electric Vehicles Charging in New Generation Power Markets based on Fuzzy Logic and the Concept of Virtual Budget,” 9th International Conference on Information, Systems and Applications (IISA), Zakynthos, Greece, July 2018, pp. 1-7. Invited
  81. Lagari, P., Weidenbenner, S., Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “Testing the sensitivity of a neural based identification algorithm to shielding levels,” American Nuclear Society Annual Meeting, Philadelphia, PA, USA, June 2018, pp. 779-782.
  82. Alamaniotis, M., & Karagiannis, G., “’Genetic Driven Multi-Relevance Vector Regression Forecasting of Hourly Wind Speed in Smart Power Systems,” IEEE PES Innovative Smart Grid Technologies – North America, 2018, pp. 1-5.
  83. Alamaniotis, M., & Tsoukalas, L.H., “Fuzzy Multi-Kernel Approach in Intelligent Control of Energy Consumption in Smart Cities,” 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2017), Boston, MA, USA, November 2017, pp. 1021-1028.
  84. Alamaniotis, M., & Tsoukalas, L.H., “Multi-Kernel Assimilation for Predictive Intervals in Nodal Short-Term Load Forecasting,” IEEE International Conference on Intelligent System Application to Power Systems (ISAP 2017), San Antonio, TX, USA, September 2017, pp. 1-6.
  85. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., “Three-Phase Line Overloading Predictive Monitoring utilizing Artificial Neural Networks,” IEEE International Conference on Intelligent System Application to Power Systems (ISAP 2017), San Antonio, TX, USA, September 2017, pp. 1-6.
  86. Alamaniotis, M., & Tsoukalas, L.H., “Utilization of Virtual Buffer in Local Area Grids for Electricity Storage in Smart Power Systems,” 49th North American Power Symposium, Morgantown, WV, USA, September 2017, pp. 1-6.
  87. Fainti, R., Alamaniotis, M., Tsoukalas, L.H., Karasimou, M., and Tsionas, I., “Ampacity Level Monitoring Utilizing Fuzzy Logic Theory in Deregulated Power Markets,” 8th International Conference on Information, Systems and Applications (IISA), Larnaca, Cyprus, August 2017, pp. 1-6.
  88. Nasiakou, A., Alamaniotis, M., Toukalas, L.H., Karagiannis, G., “A Three-Stage Scheme for Consumers’ Partitioning Using Hierarchical Clustering Algorithm,” 8th International Conference on Information, Systems and Applications (IISA), Larnaca, Cyprus, August 2017, pp. 1-6.
  89. Bourbakis, N., Alamaniotis, M., & Tsoukalas, L.H., “A Smart Car Model based on Autonomous Agents for Reducing Accidents,” IEEE Transportation Electrification Conference & Expo, Chicago, Il, USA, June 22-24, 2017, pp.767-772.
  90. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., “Anticipatory Driven Nodal Electricity Load Morphing in Smart Cities Enhancing Consumption Privacy,” IEEE PES PowerTech Conference, Manchester, UK, June 18-22, 2017, pp. 1-6.
  91. Nasiakou, A., Bean, R., & Alamaniotis, M., “Development of Human Machine Interface (HMI) for Digital Control Rooms in Nuclear Power Plants,” 10th International Topical Meeting on Nuclear Power Plant Instrumentation, Control and Human Machine Interface Technologies (NPIC & HMIT 2017), San Fracncisco, CA, June 11-15, 2017, pp. 132-141.
  92. Alamaniotis, M., & Tsoukalas, L.H., “Anticipatory System for Detection of Hidden Facilities utilizing Nodal Load Consumption Information in Smart Grids,” IEEE Global Conference on Signal and Information Processing (GlobalSip), Washington D.C., USA, December 2016, pp. 806-810.
  93. Alamaniotis, M., Tsoukalas, L.H., & Buckner, M., “Privacy-Driven Electricity Group Demand Response in Smart Cities Using Particle Swarm Optimization,” IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2016), San Jose, CA, USA, November 2016, pp. 946-953.
  94. Belligianni, F., Alamaniotis, M., Fevgas, A., Tsompanopoulou, Bozanis, P., & Tsoukalas, L.H., “An Internet of Things Architecture for Preserving Privacy of Energy Consumption,” 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.
  95. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “Power Distribution Network Partitioning in Big Data Environment Using K-Means and Fuzzy Logic,” 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.
  96. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., “Distribution Congestion Prediction Using Artificial Neural Network for Big Data,” 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.
  97. Alamaniotis, M., Nasiakou, A., Fainti, R., & Tsoukalas, L.H., “Leaky Bucket Approach Implementing Anticipatory Control for Nodal Power Flow Management in Smart Energy Systems,” IEEE PES Innovative Smart Grid Technologies, Europe (ISGT 2016), Ljubljana, Slovenia, October 9-12, 2016, pp. 1-6.
  98. Alamaniotis, M., & Tsoukalas, L.H., “Implementing Smart Energy Systems: Integrating Load and Price Forecasting for Single Parameter based Demand Response,” IEEE PES Innovative Smart Grid Technologies, Europe (ISGT 2016), Ljubljana, Slovenia, October 9-12, 2016, pp. 1-6.
  99. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “Developing Intelligent Non-proliferation Enabling Capabilities: Very-Short-Term Prediction of Background Radiation in Radioactive Source Search Using Relevance Vector Regression,” Advances in Nuclear Nonproliferation Technology and Policy Conference (ANTPC), Santa Fe, NM, September 25-30, 2016, pp. 1-4.
  100. Mattingly, J., Hutchinson, J., Sullivan, C., Stinnett, J., Kamuda, M., Alamaniotis, M., Sims, B., Mueller, J., Newby, J., Linkous, J., Pozzi, S., Polack, K., Hamel, M., He, Z., Goodman, D., & Streicher, M., “CNEC and CVT Subcritical Experiments with Category I Special Nuclear Material at the Nevada National Security Site Device Assembly Facility” Institute of Nuclear Materials Annual Conference, July 2016, pp. 1-12.
  101. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “MatGridGUI- a Toolbox for GridLAB-D Simulation Platform,” 7th International Conference on Information, Intelligence, Systems and Applications, Chalkidiki, Greece, July 2016, pp. 1-5.
  102. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., “Three-Phase Congestion Prediction Utilizing Artificial Neural Networks,” 7th International Conference on Information, Intelligence, Systems and Applications, Chalkidiki, Greece, July 2016, pp. 1-6.
  103. Lagari, P.L., Nasiakou, A., Fainti, R., Mao, K., L.H. Tsoukalas, R. Bean & Alamaniotis, M., “Evaluation of Human Machine Interface (HMI) in Nuclear Power Plants with Fuzzy Logic Method,” 7th International Conference on Information, Intelligence, Systems and Applications, Chalkidiki, Greece, July 2016, pp. 1-6.
  104. Alamaniotis, M., & Cappelli, M., “Real-Time State Identification of Boiling Water Reactors Using Relevance Vector Machines,” 24th American Society of Mechanical Engineers International Conference on Nuclear Engineering (ICONE), Charlotte, NC, USA, June 2016, pp. 8.
  105. Alamaniotis, M., & Tsoukalas, L.H., “Multi-Kernel Anticipatory Approach to Intelligent Control with Application to Load Management of Electrical Appliances,” 16th Mediterranean Conference on Control and Automation, Athens, Greece, June 21-24, 2016, pp. 1290-1295.
  106. Lagari, P.L., Mao, K., Tsoukalas, L.H., & Alamaniotis, M., “Fuzzy Logic Method for Joint Human Machine Interface Evaluation in Nuclear Power Plants,” ANS Student Conference 2016, American Nuclear Society, Madison, Wisconsin, USA, March 31-April 3, 2016, pp.1-4.
  107. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., “Very-Short Term Forecasting of Electricity Price Signals Using a Pareto Composition of Kernel Machines in Smart Power Systems,” 3rd IEEE Global Conference on Signal and Information Processing, Orlando, FL, December 2015, pp. 780-784.
  108. Alamaniotis, M., Tsoukalas, L.H., Fevgas, A., Tsompanopoulou, P., & Bozanis, P., “Multiobjective Unfolding of Shared Power Consumption Pattern using Genetic Algorithm for Estimating Individual Usage in Smart Cities,” 27th International Conference on Tools with Artificial Intelligence, Vietri Sul Mare, Italy, November 2015, pp. 398-404.
  109. Alamaniotis, M., & Tsoukalas, L.H., “Developing Intelligent Radiation Analysis Systems: A Hybrid Wave-Fuzzy Methodology for Analysis of Radiation Spectra,” 27th International Conference on Tools with Artificial Intelligence, Vietri Sul Mare, Italy, November 2015, pp. 1114-1121.
  110. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “Short-Term Gamma Background Anticipation Using Learning Gaussian Processes,” IEEE Nuclear Science Symposium & Medical Imaging Conference Record, San Diego, CA, November 2015, pp. 1-4.
  111. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “Anomaly Detection in Radiation Signals Using Kernel Machine Intelligence,” International Conference on Information, Intelligence, Systems and Applications, Corfu, Greece, July 2015, pp. 6.
  112. Alamaniotis, M., & Tsoukalas, L.H., “Anticipation of Minutes-Ahead Household Active Power Consumption Using Gaussian Processes,” International Conference on Information, Intelligence, Systems and Applications, Corfu, Greece, July 2015, pp. 6.
  113. Bourbakis, N., Ktistakis-Papadakis, I., Tsoukalas, L.H., & Alamaniotis, M., “An Autonomous Intelligent Wheelchair mounted with Robotic Arms for Smart Homes,” International Conference on Information, Intelligence, Systems and Applications, Corfu, Greece, July 2015, pp. 7.
  114. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “Data Driven Modeling of Radiation Background using an Ensemble of Learning Methods: Initial Concepts and Preliminary Results,” Transactions of the American Nuclear Society Annual Meeting, San Antonio, TX, USA, June 7-11, 2015, pp. 249-252.
  115. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., “A New Approach in Gamma Ray Spectra Analysis: Automated Integration of Peak Detection and Spectrum Fitting using Fuzzy Logic and Multiple Linear Regression,” Transactions of the American Nuclear Society Annual Meeting, San Antonio, TX, USA, June 7-11, 2015, pp. 260-263.
  116. Alamaniotis, M., Jin. X., & Ray, A., “On-line Condition Monitoring of Boiling Water Reactors Using Symbolic Dynamic Analysis,” 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 722-732.
  117. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H., “An Operator’s Support System for Reactor Transients using Fuzzy Logic,” 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 2148-2154.
  118. Alamaniotis, M., Tsoukalas, L.H., & Agarwal, V., “Predictive based Monitoring of Nuclear Plant Component Degradation Using Support Vector Regression,” 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 1199-1207.
  119. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H., “A Bayesian Approach to Monitoring Spent Fuel Using Cosmic Ray Muons,” American Nuclear Society Winter Meeting and Nuclear Technology Expo, November 9-13, 2014, Anaheim, CA, USA, pp. 369-370.
  120. Alamaniotis, M., & Tsoukalas, L.H., “Integration of Price Anticipation and Self-Elasticity for Hour-Ahead Electricity Bidding and Purchasing,” 9th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion: (MEDPOWER 2014), November 2014, Piraeus, Greece, pp. 1-4.
  121. Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., “Monthly Load Forecasting Using Gaussian Process Regression,” 9th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2014), November 2014, Piraeus, Greece, pp. 1-7.
  122. Alamaniotis, M., Tsoukalas, L.H.., & Bourbakis, N., “Virtual Cost Approach: Electricity Consumption Scheduling in Smart Grids for Price Directed Electricity Markets,” 5th International Conference on Information, Intelligence, Systems and Applications, July 2014, Chania, Greece, pp. 38-43.
  123. Alamaniotis, M., Agarwal, V., & Jevremovic, T., “Anticipatory Monitoring and Control of Complex Energy Systems Using a Fuzzy based Fusion of Support Vector Regressors,” 5th International Conference on Information, Intelligence, Systems and Applications, July 2014, Chania, Greece, pp. 33-37.
  124. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H. “Creep Rupture Forecasting for high Performance Energy Systems,” 5th International Conference on Information, Intelligence, Systems and Applications, July 2014, Chania, Greece, pp. 95-99. Best Paper Award
  125. Alamaniotis, M., Hernandez, H., & Jevremovic, T., “Role of Nuclear Forensics defined as a Digital Problem with Neurofuzzy Approach in various Applications,” American Institute of Chemical Engineers Annual Conference 2013 (AIChE 2013), San Fransisco, CA, USA November 2013, pp. 1-7.
  126. Alamaniotis, M., & Tsoukalas, L., “Layered based Approach to Virtual Storage for Smart Power Systems,” 4th International Conference on Information, Intelligence, Systems and Applications, July 2013, Piraeus, Greece, pp. 22-27.
  127. Alamaniotis, M., Hernandez, H., & Jevremovic, T., “Application of Support Vector Regression in Removing Poisson Fluctuation from Pulse Height Gamma-Ray Spectra,” 4th International Conference on Information, Intelligence, Systems and Applications, July 2013, Piraeus, Greece, pp. 18-21.
  128. Owen, L., Alamaniotis, M., & Jevremovic, T., “Automated Signal-Layered Algorithm for Processing Complex Gamma Radiation Spectra,” American Nuclear Society Student Conference, April 2013, Boston, MA, USA, pp. 1-5. In Best 4 Conference Papers
  129. Painter, D., Alamaniotis, M., & Jevremovic, T., “Analysis of City Grown Bing Cherry Trees using Neutron Activation Analysis,” American Nuclear Society Student Conference, April 2013, Boston, MA, USA, pp.1-5.
  130. Santora, J., Alamaniotis, M., & Jevremovic, T., “Viticulture Improvement Applicable to all Plants,” American Nuclear Society Student Conference, April 2013, Boston, MA, USA, pp. 1-5.
  131. Alamaniotis, M., Ikonomopoulos, A., Alamaniotis, A., Bargiotas, D., & Tsoukalas, L.H., “Day-ahead Electricity Price Forecasting using Optimized Multiple-Regression of Relevance Vector Machines,” 8th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2012), October 2012, Cagliari, Italy, pp. 1-6.
  132. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H., “Background Spectrum Estimation for Low Count Spectra Using Kernel-Modeled Gaussian Processes,” American Nuclear Society Annual Meeting, June 2012, Chicago, IL, USA, pp. 273-274.
  133. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H., “Fuzzy Logic Radio Isotope Identifier for Gamma Spectra Analysis in Source Search Applications,” American Nuclear Society Annual Meeting, June 2012, Chicago, IL, USA, pp. 211-212.
  134. Young, J., Alamaniotis, M., Gao, R., & Tsoukalas, L.H., “Development of Path Search Toolkit for Nuclear Non-Proliferation Applications,” American Nuclear Society Annual Meeting, June 2012, Chicago, IL, USA, pp. 205-206.
  135. Young, J, Alamaniotis, M., & Tsoukalas, L.H., “Fuzzy Logic Detection of Special Nuclear Materials in Aqueous Environments,” American Nuclear Society Student Conference, Las Vegas, NV, April 2012, pp. 1-2.
  136. Alamaniotis, M., Ikonomopoulos, A., Gao, R., & Tsoukalas, L.H., “Lessons learned in Accidents: An Intelligent Systems Perspective for Nuclear Power Plant Safety,” American Nuclear Society Winter Meeting, Washington D.C., 2011, pp. 305.
  137. Alamaniotis, M., Ikonomopoulos., & Tsoukalas, L.H., “A Pareto Optimization Approach of a Gaussian Process Ensemble for Short-Term Load Forecasting,” International Conference on Intelligent System Applications on Power Systems (ISAP 2011), Crete, Greece, September 2011, pp. 48(1-6).
  138. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Online Surveillance of Nuclear Power Plant Peripheral Components using Support Vector Regression,” International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems (ICI 2011), Daejeon, Korea, August 2011, pp. 1230(1-6).
  139. Alamaniotis, M., Xiao, S., Young, J., Gao, R., Tsoukalas, L.H., Choe, D., & Jevremovic T., “Using iMASS to simulate the Tracking/Movement of Special Nuclear Materials,” American Institute of Chemical Engineers Annual Conference (AIChE 2010), Salt Lake City, UT, November 2010, pp. 1-6.
  140. Alamaniotis, M., Gao, R., & Tsoukalas, L.H., “Towards an Energy Internet: A Game-Theoretic Approach to Price-Directed Energy Utilization,” 1st International ICST Conference on E-Energy, Athens, Greece, October 2010, pp. 3-10.
  141. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., “Distributed System for Operator Support in Nuclear Power Plants,” 1st International Conference for Undergraduate and Postgraduate Students in Computer Engineering, Informatics, related Technologies and Applications: EUREKA 2010, Patras, Greece, October 2010, pp. 1-9.
  142. Alamaniotis, M., Tsoukalas, L.H., & Ikonomopoulos, A., “Automated System for Plan Realization in Nuclear Power Plants,” European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, September 2010, pp. 2103-2109.
  143. Alamaniotis, M., Ikonomopoulos, A. & Tsoukalas, L.H., “Gaussian Processes for Failure Prediction of Slow Degradation Components in Nuclear Power Plants”, European Safety and Reliability Conference (ESREL 2010), Rhodes, Greece, September 2010, pp. 2096-2102.
  144. Alamaniotis, M., Young, J., Tsoukalas, L.H., & Jevremovic, T., “Assessment of Wavelet Processing in Removing Background Peaks from NRF Spectra,” American Nuclear Society (ANS) Student Conference, Ann Arbor, MI, April 2010, pp. 1-2.
  145. Alamaniotis, M., Young, J., Tsoukalas, L.H., & Jevremovic, T., “An Insight in Wavelet Denoising of Nuclear Resonance Spectra for Identification of Hazardous Materials,” 1st National Conference on Advanced Tools and Solutions for Nuclear Material Detection, Salt Lake City, UT, March 2010, pp. 1-6.
  146. Alamaniotis, M., Gao, R., Tsoukalas, L.H., & Jevremovic, T., “Expert System for Decision Making and Instructing Nuclear Resonance Fluorescence Cargo Interrogation,” 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, November 2009, pp. 666-673.
  147. Alamaniotis, M., Gao, R., Tsoukalas, L.H., & Jevremovic, T., “Intelligent Order-based Method for Synthesis of NRF Spectra and Detection of Hazardous Materials,” 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, November 2009, pp. 658-665.
  148. Alamaniotis, M., Young, J., Perry, J., Xiao, S., Agarwal, V., Forsberg, P., Gao, R., Choi, C., 4Tsoukalas, L.H., & Jevremovic, T., “Engineering Solution to Nuclear Material Detection at Ports: Introducing the Novel iMASS Paradigm,” 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, November 2009, pp. 679-682.
  149. Pantelopoulos, A., Alamaniotis, M., Jevremovic, T., Park, M.S., Chung, M.S., & Bourbakis N., “LG-Graph based Detection of NRF Signatures: Initial Results and Comparison,” 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, November 2009, pp. 683-686.
  150. Alamaniotis, M., Youtsos, M., Gao, R., & Tsoukalas L.H., “Pseudo Neural Network based Diagnostic System for Two Phase Annular Flow in Nuclear Power Plants,” International Conference on Optimization Using Exergy-based Methods and Computational Fluid Dynamics, Berlin, Germany, October 2009, pp. 203-208.
  151. Alamaniotis, M., Gao, R., & Tsoukalas, L.H., “Distributed Intelligence System for Online Action Taking in Non-Anticipated Situations in Nuclear Power Plants,” ICAPS-2009 Scheduling and Planning Applications Workshop, Thessaloniki, Greece, September 2009, pp. 7-13.
  152. Alamaniotis, M., Gao, R., Jevremovic, T., & Tsoukalas, L.H., “Intelligent Detection of SNM in Liquid Containers,” 16th International Conference on Systems, Signals and Image Processing, Chalkida, Greece, June 2009, pp. 1-4.
  153. Alamaniotis, M., Terrill, S., Gao, R., & Jevremovic, T., “Automated Multisignal Detection of Special Nuclear Material in Cargo Containers,” American Nuclear Society (ANS) Student Conference, Gainesville, Florida, April 2009, pp. 1-2. Best Paper Award
  154. Pantelopoulos, A., Alamaniotis, M., Bourbakis, N., & Jevremovic, T., “Heuristic Identification of Nuclear Materials from NRF Spectra”, American Nuclear Society (ANS) Student Conference, Gainesville, Florida, April 2009, pp. 1-2.

Abstracts in Journals

  1. Miserlis, D., Munian, Y., Cardona, L.M.F., Teixeira, P.G., DuBose, J.J., Davies, M.G., Bohannon, W., Koutakis, P., & Alamaniotis, M., “Benchmarking EfficientNetB7, InceptionResNetV2, InceptionV3, and xception artificial neural networks applications for aortic pathologies analysis,” Journal of Vascular Surgery, vol. 77(6), 2023, p.e345.
  2. Miserlis, D., Munian, Y., Fletcher, E., Crapps, J., Teixeira, P., Ferrer, L., DuBose, J., Bohannon, W.T., Monteleone, P., Alamaniotis, M. and Koutakis, P., “Evaluating The Diagnostic Ability Of Six Different Artificial Neural Networks From The Subcellular Microenvironment To The Clinical Manifestation,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 43(Suppl_1), 2023, pp.A544-A544.

Technical Reports

  1. Valdez, L., Alamaniotis, M., & Heifetz, A., “Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator,” Argonne National Laboratory, ANL/NSE-22/78, September 2022, pp. 1-15.

Newsletter Articles

  1. Alamaniotis, M., “Vision of Smart Cities as a means for Implementing Smart Nuclear Security,” IEEE Smart Cities Newsletter, January 2023, pp. 19-24.

Book Reviews

  1. Alamaniotis, M., Review of “Handbook on Artificial Intelligence-Empowered Applied Software Engineering, Vol. 1-2” by Virvou, M., Tsihrintzis, G.A., Bourbakis, N.G., Jain, L.C., Intelligent Decision Technologies: An International Journal, vol.X(X), March 2023, pp. 1-2. In Press

Editorials

  1. Pan, S., & Alamaniotis, M., “Special Issue on Selected Papers from the 32nd Annual Conference on Tools with Artificial Intelligence (ICTAI 2020),” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 31 (7), November 2022, pp. (2202007) 1.
  2. Alamaniotis, M., “Message from the IEEE ICTAI 2020 General Chair,” 32nd International Conference on Tools with Artificial Intelligence (ICTAI 2020), November 2020, pp. xxvii.
  3. Alamaniotis, M., “Special Issue on Selected Papers from the 30th Annual Conference on Tools with Artificial Intelligence (ICTAI 2018),” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 26 (5), June 2019, pp. (#2002002)1-1.
  4. Alamaniotis, M., “Message from the Program Chair,” 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018), November 2018, pp. 22-22.
  5. Bourbakis, N., & Alamaniotis, M., “Message from the Applications of AI in Smart Cities Track Chairs,” 30th International Conference on Tools with Artificial Intelligence (ICTAI 2018), November 2018, pp. 24-24.
  6. Mali, A., & Alamaniotis, M., “Special Issue from ICTAI 2016”, International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 26 (5), October 2017, pp. (#1702004)1-1.
  7. Mali, A., & Alamaniotis, M., “Special Issue from ICTAI 2016”, International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 26 (5), October 2017, pp. (#1702004)1-1.