{"id":14,"date":"2017-07-07T20:21:07","date_gmt":"2017-07-07T20:21:07","guid":{"rendered":"http:\/\/utsaengineer.wpengine.com\/faculty-page-example\/?page_id=14"},"modified":"2026-04-04T05:50:34","modified_gmt":"2026-04-04T05:50:34","slug":"publications","status":"publish","type":"page","link":"https:\/\/ceid.utsa.edu\/malamaniotis\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

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,\u00a0 and viii) Editorials<\/p>\r\n

Journal Papers<\/strong><\/p>\r\n

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  1. De Leon, R., & Alamaniotis, M. \u201cLocal-Global Graph Framework for Gamma Signature Spectrum Identification Supporting Interpretable Decisions in Nuclear Security,\u201d Intelligent Decision Technologies<\/em>, Sage, February 2026, pp. 1-20. Accepted<\/strong><\/li>\r\n
  2. Gharibshahi E., Alamaniotis, M., \u201cSimulation Study of Muon Tomography for enhancing the Nuclear Security of Transportable Microreactors,\u201d Nuclear Engineering and Technology<\/em>, Elsevier, June 2026, vol. 6, pp. 104192(1-15).<\/li>\r\n
  3. Gonzalez, R., Ayyagari, K.S., Almoola, A., Aryasomyajula, V.A., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cOptimal Protection Coordination of Dual-Setting Relays with Inverse-Time and Definite-Time Characteristics,\u201d IEEE Open Access Journal of Power and Energy<\/em>, December 2025, vol. 12, pp. 882-894.<\/li>\r\n
  4. Arvanitidis, A., Talbot, P., Gatsis, N., & Alamaniotis, M. \u201cComprehensive Assessment of Deep Reinforcement Learning Approaches for Economic Dispatch in Nuclear-Driven Microgrids,\u201d Computers and Electrical Engineering<\/em>, Elsevier, August 2025, vol. 125, pp. 110528(1-24).<\/li>\r\n
  5. Arvanitidis, A., & Alamaniotis, M. \u201cOptimal Economic Dispatch and Load-Following Strategies for Nuclear Integrated Energy Systems,\u201d IEEE Access<\/em>, July 2025, vol. 13, pp. 118857-118873.<\/li>\r\n
  6. Valdez, L., Alamaniotis, M., & Heifetz, A., \u201cIdentification of Distorted Gamma Signature Patterns using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network,\u201d Nuclear Technology<\/em>, Taylor & Francis, July 2025, vol. 211, pp. 1423-1437.<\/li>\r\n
  7. Alamaniotis, M., & Ipiotis, K., \u201cArtificial Intelligence as Enabler for Adoption of Sustainable Nuclear-Powered Maritime Ships: Challenges and Opportunities,\u201d Sustainability, <\/em>MDPI, March 2025, vol. 17(8), pp. 3654(1-21).<\/li>\r\n
  8. Alamaniotis, M., \u201cData-Driven Modeling of Background Radiation Structure Utilizing Matrix Profile in Nuclear Security,\u201d Scientific Reports, <\/em>Nature, January 2025, vol. 15, pp. 3740(1-15).<\/li>\r\n
  9. Gharibshahi, E., & Alamaniotis, M., \u201cSimulation Study of Cosmic-Ray Muons for Detection of Nuclear Materials in Liquid Freight Containers,\u201d IOP Journal of Instrumentation, January 2025, pp. 1-26.\u00a0<\/li>\r\n
  10. Valdez, L., Alamaniotis, M., Moore, E., & Heifetz, A., \u201cDetection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks,\u201d Nuclear Technology<\/em>, Taylor and Francis, January 2025, pp. 1-12.<\/li>\r\n
  11. Alamaniotis, M., \u201cArtificial Intelligence Management System of Floating Nuclear Reactors for Implementing Remote Distributed Energy Environments,\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, December 2024, pp. 1-21. Accepted<\/em><\/strong><\/li>\r\n
  12. Alamaniotis, M., \u201cMode-Driven Explainable Artificial Intelligence Approach for Estimating Background Radiation Spectrum in a Measurement Applicable to Nuclear Security,\u201d Annals of Nuclear Energy<\/em>, Elsevier, August 2024, pp. 1-21.<\/li>\r\n
  13. Faubel, C., Arvanitidis, A.I., Iskandar, L., Martinez-Molina, A., & Alamaniotis, M., \u201cComparative analysis of artificial intelligence models for real-time and future forecasting of environmental conditions: A wood-frame historic building case study,\u201d Journal of Building Engineering<\/em>, Elsevier, December 2024, pp. 1-30.<\/li>\r\n
  14. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., \u201cExamining the Impact of Natural Ventilation versus Heat Recovery Ventilation Systems on Indoor Air Quality: A Tiny House Case Study,\u201d Buildings<\/em>, MDPI, June 2024, pp. 1802 (1-12).<\/li>\r\n
  15. Karaiskos, P., Martinez-Molina, A., Munian, Y, & Alamaniotis, M., \u201cIndoor Air Quality Prediction Modeling for a Naturally Ventilated Fitness Building Using RNN-LSTM Artificial Neural Networks,\u201d Smart and Sustainable Built Environment<\/em>, Emerald Publishing, July 2024, pp. 1-23.<\/li>\r\n
  16. Arvanitidis, A.I., Alamaniotis, M., \u201cIntegrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems,\u201d Energies<\/em>, MDPI,\u00a0 2024, pp. 1-31.<\/li>\r\n
  17. Gharibshahi, E., & Alamaniotis, M., \u201cArtificial Intelligence Detection System of Radioactive Nanocomposites in Liquid Filled Containers for Nuclear Security,\u201d Nuclear Technology<\/em>, Taylor and Francis, May 2024, vol. 210, pp. 868-883.<\/li>\r\n
  18. Shahabinejad, H., Sudac, D., Alamaniotis, M., Nad, K., & Obhodas, J., \u201cPrecise Gamma-ray Stabilization Using Full Spectral Information,\u201d Radiation Physics and Chemistry<\/em>, Elsevier, February 2024, vol. 215 pp. 111337(1-6).\u00a0<\/li>\r\n
  19. Alamaniotis, M., & Alexiou, M, \u201cFuzzy Leaky Bucket Approach for Large Scale Social Driven Energy Allocation in Emergencies in Smart City Zones,\u201d Electronics<\/em>, MDPI, 2024, vol. 13(4), pp. 722(1-20).\u00a0<\/li>\r\n
  20. Gu, S., & Alamaniotis, M., \u201cSequential Deployment of Mobile Radiation Sensor Network using Reinforcement Learning in Radioactive Source Search,\u201d Nuclear Technology<\/em>, Taylor and Francis, January 2024, vol. 210(1), pp. 100-111.<\/li>\r\n
  21. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., \u201cIndoor Air Quality Investigations in a Naturally Ventilated Cross-Training Sports Center. A Case Study,\u201d Journal of <\/em>Building Engineering<\/em>, Elsevier, October 2023, vol. 77, pp. 107457(1-13).\u00a0<\/li>\r\n
  22. Mathew, J., Shirsagar, R., Abidin, D.Z., Griffin, J., Kanarachos, S., James, J., Alamaniotis, M., & Fitzpatrick, M.E., \u201cA comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data,\u201d Scientific Reports<\/em>, Nature, June 2023, vol. 13, pp. 9448(1-15).<\/li>\r\n
  23. Gonzalez, R., Aryasomyajula, V.A., Ayyagari, K.S., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cModeling and Studying the Impact of Dynamic Reactive Current Limiting in Grid-Following Inverters for Distribution Network Protection,\u201d Electric Power Systems Research<\/em>, Elsevier, July 2023, vol. 224, pp. 109609(1-8).<\/li>\r\n
  24. Arvanitidis, A.I., Agarwal, V., & Alamaniotis, M., \u201cNuclear-Driven Integrated Energy Systems: A State-of-the-Art Review,\u201d Energies<\/em>, MDPI, May 2023, vol. 16(11), pp. 4293(1-23).<\/li>\r\n
  25. Gonzalez, R., Ahmed, S., Alamaniotis, M., \u201cImplementing Very-Short-Term Forecasting of Residential Load Demand using a Deep Neural Network Architecture,\u201d Energies<\/em>, MDPI, April 2023, vol. 16(9), pp. 3636(1-16)<\/li>\r\n
  26. Nichiforov, C., & Alamaniotis, M., \u201cLearning Matrix Profile Method for Discord-Based Attribution of Complex Consumption Behavior,\u201d Cogent Engineering<\/em>, Taylor & Francis, vol. 10, April 2023, pp. 2199518(1-16).<\/li>\r\n
  27. Akritidis, L., Alamaniotis, M., & Bozanis, P., \u201cFLAGR: A Flexible High-Performance Library for Rank Aggregation\u201d SoftwareX<\/em>, Elsevier, vol. 21, February 2023, pp. 101319(1-6).<\/li>\r\n
  28. Ayyagari, K.S., Gonzalez, R., Jin, Y., Alamaniotis, M., Ahmed, S., & Gatsis, N., \u201cLearning CVaR-Optimal Reactive Power Control Policies in Distribution Systems Using Artificial Neural Networks,\u201d Journal of Modern Power Systems and Clean Energy<\/em>, SGEPRI, vol. 11(1), January 2023, pp. 201-211.<\/li>\r\n
  29. Nichiforov, C., Martinez-Molina, A., & Alamaniotis, M., \u201cAn Intelligent Big Data Analytics Method for Two-Dimensional Non-Residential Building Energy Forecasting,\u201d\u00a0Intelligent Decision Technologies<\/em>, IOS Press, 15(4), December 2022 pp. 1-9. Accepted<\/em><\/li>\r\n
  30. Qiao, Y., Chen, S., Alinizzi, M., & Alamaniotis, M., Labi, S., \u201cInvestigating the efficacy of machine learning techniques for IRI estimation based on pavement distress type, density, and severity,\u201d Journal of Infrastructure systems<\/em>, ASCE, vol. 28(4), December 2022, pp 04022035(1-18).<\/li>\r\n
  31. Dimitroulis, P., & Alamaniotis, M., \u201cMultimodal Energy Management System for Residential Building Prosumers Utilizing Various Lifestyles,\u201d Electric Power Systems Research<\/em>, Elsevier, December 2022, 108737(1-24).<\/li>\r\n
  32. Arvanitidis, A.I., Bargiotas, D., Kontogiannis, D., Fevgas, A., & Alamaniotis, M., \u201cOptimized Data-Driven Models for Short-Term Electricity Price Forecasting based on Signal Decomposition and Clustering Techniques,\u201d Energies<\/em>, MDPI, vol. 15, October 2022, pp. (7929)1-25.<\/li>\r\n
  33. Mowen, D., Munian, Y., & Alamaniotis, M., \u201cImproving Road Safety during Nocturnal Hours by characterizing Animal Poses utilizing CNN-based Analysis of Thermal Images,\u201d Sustainability<\/em>, MDPI, vol. 14(19), October 2022, 12133(1-15). Editor\u2019s Selection Article<\/em><\/li>\r\n
  34. Akritidis, L., Alamaniotis, M., Fevgas, A., Tsompanopoulou, P., & Bozanis, P., \u201cImproving Hierarchical Short Text Clustering through Dominant Feature Learning,\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 31 (5), August 2022, pp. (#2250034)1-24.<\/li>\r\n
  35. Gharibshahi, E., & Alamaniotis, M., \u201cModeling and simulation of radioactive nanomaterials of Pb-U, Pb-Th, and Pb-Co in liquid containers for nuclear security applications,\u201d Nuclear Science and Engineering<\/em>, Taylor and Francis, vol. 196 (8), June 2022, pp. 1006-1019.<\/li>\r\n
  36. Alamaniotis, M., \u201cSmart Data Analytic Based Transform of Low-Count Gamma-Ray Spectra for Enhancing Isotope Detection using a Self-Learning Window Driven Relevance Vector Regression,\u201d IEEE Transaction on Nuclear Science<\/em>, vol. 69(6), June 2022, pp. 1357-1365.<\/li>\r\n
  37. Gharibshahi, E., & Alamaniotis, M., \u201cSimulation and Modeling of Optical Properties of U, Th, Pb, and Co Nanoparticles of Interest to Nuclear Security using Finite Element Analysis,\u201d Nanomaterials<\/em>, MDPI, vol. 12(10), May 2022, pp. 1710 (1-13).<\/li>\r\n
  38. Munian, Y., Martinez-Molina, A., Miserlis, D., Hernandez, H., & Alamaniotis, M., \u201cA HOG-CNN Based System for Detection of Wild Animals in Nocturnal Hours with Application to Automobiles,\u201d Applied Artificial Intelligence<\/em>, CRC Taylor, vol. 36(1), 2022, pp. (e2031825) 1978-2006.<\/li>\r\n
  39. Dimitroulis, P., & Alamaniotis, M., \u201cA Fuzzy Logic Energy Management System of On-Grid Electrical System for Residential Prosumers,\u201d Electric Power Systems Research<\/em>, Elsevier, vol. 2022, January 2022, pp. 107621(1-14).<\/li>\r\n
  40. Munian, Y., Martinez-Molina, A., & Alamaniotis, M., \u201cAn AI Driven Arousal System to Alert and Avoid the Crepuscular Animal-based Vehicle Collision,\u201d Intelligent Decision Technologies<\/em>, IOS Press, 15(4), December 2021, pp. 707-720.<\/li>\r\n
  41. Nichiforov, C., Martinez-Molina, A., & Alamaniotis, M., \u201cAn Intelligent Approach for Performing Energy Driven Identification of Buildings Utilizing Joint Electricity-Gas Patterns,\u201d Energies \u2013 Special Issue on AI for Buildings<\/em>, vol. 14(22), 2021, 7465(1-11).<\/li>\r\n
  42. Le, V., Ramirez, J., & Alamaniotis, M., \u201cIntelligent Room based Identification of Electricity Consumption with Ensemble Learning Method in Smart Energy,\u201d Energies \u2013 Special Issue on Intelligent Energy Systems and Energy Policy<\/em>, vol. 14(20), 2021, pp. 6717(1-13).<\/li>\r\n
  43. Campos, B., & Alamaniotis, M., \u201cReview of Internal Cyber Attacks in Nuclear Facilities and an Artificial Neural Network Model for Implementing Cyberforensics,\u201d Nuclear Technology and Radiation Protection<\/em>, Vinca Institute, vol. XXXVI(2), June 2021, pp. 128-138.<\/li>\r\n
  44. Holbrook, L., & Alamaniotis, M., \u201cSurvey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices,\u201d International Journal on Tools with Artificial Intelligence, <\/em>World Scientific Publishing Company, June 2021, vol. 30(4), pp. 2150021(1-21).<\/li>\r\n
  45. Fevgas, A., Akritidis, L., Alamaniotis, M., Tsompanopoulou, P., & Bozanis, P., \u201cHyR-tree: A Spatial index for hybrid Flash\/3DXPoint Storage,\u201d Neural Computing and Applications \u2013 Special Issue on Information, Intelligence, Systems and Applications, <\/em>Springer, 2021, pp. 1-13.<\/li>\r\n
  46. Miserlis, D., Jafari, A., M. Davies, Guda, T. & Alamaniotis, M., \u201cArtificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey,\u201d American Journal of Biomedical Science and Research,<\/em> 12(2), 2021, pp. 182-188.<\/li>\r\n
  47. Khan, A.A, Berg, O., Alamaniotis, M., & Ahmed, S., \u201cIntelligent Anomaly Identification in Cyber-Physical Inverter-based Systems,\u201d Electric Power Systems Research, <\/em>Elsevier, April 2021, vol. 193, pp. (107024)1-13.<\/li>\r\n
  48. Lagari, L., Pantopoulou, S., Alamaniotis, M., & Tsoukalas, L., \u201cA Library of Radionuclide \u03b3-ray Profiles for the Identification of Unknown Sources,\u201d Nuclear Technology, <\/em>Taylor and Francis, 2021, pp. 10<\/li>\r\n
  49. Martinez-Molina, A., & Alamaniotis, M., \u201cEnhancing Historic Building Performance with the Use of Fuzzy Inference System to Control the Electric Cooling System,\u201d Sustainability,<\/em> MDPI, July 2020, vol. 12(14), pp. 58848(1-14).\u00a0<\/li>\r\n
  50. Ebrahimi, N., Guda, T., Alamaniotis, M., Miserlis, D., & Jafari, A., \u201cDesign Optimization of a Novel Networked Electromagnetic Soft Actuators System Based on Branch and Bound Algorithm,\u201d IEEE Access,<\/em> June 2020, vol. 8, pp. 119324-119335.<\/li>\r\n
  51. Alamaniotis, M., \u201cFuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids,\u201d Frontiers in Artificial Intelligence \u2013 Fuzzy Systems<\/em>, January 2020, vol. 3(1), pp. 14.\u00a0<\/li>\r\n
  52. Alamaniotis, M., & Karagiannis, G., \u201cApplication of Fuzzy Multiplexing of Learning Gaussian Processes for the Interval Forecasting of Wind Speed,\u201d IET Renewable Power Generation \u2013 Special Issue from Medpower 2018<\/em>, January 2020, vol. 14 (1), pp. 100-109.<\/li>\r\n
  53. Alamaniotis, M., & Gatsis, N., \u201cEvolutionary Multiobjective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition,\u201d Energies<\/em> \u2013 Special Issue Selected Papers from Medpower 2018<\/em>, MDPI, June 2019, vol. 12, pp. (2470) 1-18.<\/li>\r\n
  54. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., \u201cEnhancing Privacy in Smart Cities through Morphing of Anticipated Demand Utilizing Self-Elasticity and Genetic Algorithms,\u201d Sustainable Cities and Society<\/em>, Elsevier, vol. 46, April 2019, pp. (101426)1-12.<\/li>\r\n
  55. Mathew, J., Griffin, J., Alamaniotis, M., Kanarachos, S., & Fitzpatrick, M., \u201cPrediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems,\u201d Applied Soft Computing Journal<\/em>, Elsevier, vol. 70, September 2019, pp. 131-146.<\/li>\r\n
  56. Alamaniotis, M., Mathew, J., Chroneos, A., Fitzpatrick, M., & Tsoukalas, L.H., \u201cProbabilistic Kernel Machines for Predictive Monitoring of Weld Residual Stress in Energy Systems,\u201d Engineering Applications of Artificial Intelligence<\/em>, Elsevier, vol. 71, May 2018, pp. 138-154.<\/li>\r\n
  57. Mathew, J., Parfitt, D., Wilford, K., Riddle, N., Alamaniotis, M., Chroneos, A., Fitzpatrick, M., \u201cReactor Pressure Vessel Embrittlement: Insights from Neural Network Modelling,\u201d Journal of Nuclear Materials<\/em>, Elsevier, vol. 502, April 2018, pp. 311-322.<\/li>\r\n
  58. Alamaniotis, M., Gatsis, N., & Tsoukalas, L.H., \u201cVirtual Budget: Integration of Electricity Load and Price Anticipation for Load Morphing in Price-Directed Energy Utilization,\u201d Electric Power Systems Research<\/em>, Elsevier, vol. 158, May 2018, pp. 284-296.<\/li>\r\n
  59. Alamaniotis, M., & Cappelli, M., \u201cIntelligent Identification of Boiling Water Reactor State Utilizing Relevance Vector Machines,\u201d ASME Journal of Nuclear Engineering and Radiation Science<\/em>, American Society of Mechanical Engineers, vol. 4, April 2018, pp. (020904)1-9.<\/li>\r\n
  60. Alamaniotis, M., & Karagiannis, G., \u201cIntegration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI-Global, vol. 5(3), pp. 1-14.<\/li>\r\n
  61. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., \u201cExtending the K-means Clustering Algorithm to improve the Compactness of the Clusters,\u201d Journal of Pattern Recognition Research<\/em>, vol. 11(1), pp. 61-73, 2016.<\/li>\r\n
  62. Fainti, R., Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., \u201cHierarchical Method based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI-Global, vol. 4(4), October 2016, pp. 20-32.<\/li>\r\n
  63. Lagari, P.L., Sobes, V., Alamaniotis, M., & Tsoukalas, L.H., \u201cApplication of Artificial Neural Networks for Reliable Nuclear Data for Nonproliferation Modeling and Simulation,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI-Global, vol. 4(4), October 2016, pp. 54-64.<\/li>\r\n
  64. Alamaniotis, M., & Tsoukalas, L.H., \u201cFusion of Gaussian Process Kernel Regressors for Fault Prediction in Intelligent Energy Systems,\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 25 (4), August 2016, pp. (#1650023)1-17.<\/li>\r\n
  65. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., \u201cBackpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global, vol. 4(3), July 2016, pp. 1-20.<\/li>\r\n
  66. Lagari, L., Nasiakou, A., & Alamaniotis, M., \u201cEvaluation of Human Machine Interface (HMI) on a Digital and Analog Control Room in Nuclear Power Plants Using a Fuzzy Logic Approach,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global, 2016, vol. 4(2), April 2016, pp. 50-68.<\/li>\r\n
  67. Alamaniotis, M., Bargiotas, D., & Tsoukalas, L.H., \u201cTowards Smart Energy Systems: Application of Kernel Machine Regression for Medium Term Electricity Load Forecasting,\u201d SpringerPlus \u2013 Engineering Section<\/em>, Springer, vol. 5 (1), January 2016, pp. 1-15.<\/li>\r\n
  68. Eklund, M., Alamaniotis, M., Hernandez, H., & Jevremovic, T., \u201cMethod of Characteristics – A Review with Application to Science and Nuclear Engineering Computation,\u201d Progress in Nuclear Energy<\/em>, Elsevier, vol. 85, November 2015, pp. 548-567.<\/li>\r\n
  69. Chrysikou, V., Alamaniotis, M., & Tsoukalas, L.H., \u201cA Review of Incentive based Demand Response Methods in Smart Electricity Grids,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global Publications, October 2015, pp. 62-73.<\/li>\r\n
  70. Alamaniotis, M., Bargiotas, D., Bourbakis, N., & Tsoukalas, L.H., \u201cGenetic Optimal Regression of Relevance Vector Machines for Electricity Price Forecasting in Smart Grids,\u201d IEEE Transactions on Smart Grid,<\/em> Institute of Electrical and Electronic Engineers, vol. 6(6), November 2015, pp. 2997-3005.<\/li>\r\n
  71. Alamaniotis, M., Lee, S., & Jevremovic, T., \u201cIntelligent Analysis of Low Count Scintillation Spectra using Support Vector Regression and Fuzzy Logic,\u201d Nuclear Technology<\/em>, American Nuclear Society, vol. 191 (1), July 2015, pp. 41-57.<\/li>\r\n
  72. Alamaniotis, M., & Jevremovic, T., \u201cHybrid Fuzzy-Genetic Approach Integrating Peak Identification and Spectrum Fitting for Complex Gamma-Ray Spectra Analysis,\u201d IEEE Transactions on Nuclear Science<\/em>, vol. 62(3), June 2015, pp. 1262-1277.<\/li>\r\n
  73. Alamaniotis, M., Choi, C. & Tsoukalas, L.H., \u201cApplication of Fireworks Algorithm in Gamma-Ray Spectrum Fitting for Radioisotope Identification,\u201d International Journal of Swarm Intelligence Research \u2013 Special Issue on Developments and Applications of Fireworks Algorithm<\/em>, IGI Global Publications, vol. 6 (2), April-June 2015, pp. 102-125.<\/li>\r\n
  74. Bourbakis, N., Tsoukalas, L.H., Alamaniotis, M., Gao, R., & Kerkman, K., \u201cDEMOS: A Distributed Model based on Autonomous, Intelligent Agents with Monitoring and Anticipatory Responses for Energy Management in Smart Cities,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global Publications, vol. 2(4), October-December 2014, pp. 80-98.<\/li>\r\n
  75. Alamaniotis, M., Grelle, A., & Tsoukalas L., \u201cRegression to Fuzziness Method for Estimation of Remaining Useful Life in Power Plant Components,\u201d Mechanical Systems and Signal Processing<\/em>, Elsevier, vol. 48 (1-2), October 2014, pp. 188-198.<\/li>\r\n
  76. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L., \u201cCreep Rupture Forecasting: A Machine Learning Approach to Useful Life Estimation,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global Publications, vol. 2(2), April-June 2014, pp. 1-25.<\/li>\r\n
  77. Alamaniotis, M., & Agarwal, V., \u201cFuzzy Integration of Support Vector Regressor Models for Anticipatory Control of Complex Energy Systems,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global Publications, vol. 2(2), April-June 2014, pp.26-40.<\/li>\r\n
  78. Alamaniotis, M., Young, J., & Tsoukalas, L.H., \u201cAssessment of Fuzzy Logic Radioisotopic Pattern Identifier on Gamma-Ray Signals with Application to Security,\u201d International Journal of Monitoring and Surveillance Technologies Research, IGI Global<\/em>, vol. 2(1), January-March 2014, pp.1-10. [Also selected and included by Publisher in the edited Book \u201cResearch Methods: Concepts, Methodologies, Tools and Applications\u201d as Chapter 46].<\/li>\r\n
  79. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H, \u201cFuzzy-Logic Radioisotope Identifier for Gamma Spectroscopy in Source Search,\u201d IEEE Transactions on Nuclear Science<\/em>, vol. 60 (4), August 2013, pp. 3014-3024.<\/li>\r\n
  80. Alamaniotis, M., Mattingly, J., & Tsoukalas, L.H., \u201cPareto Optimal Gamma Spectroscopic Radionuclide Identification using Evolutionary Computing,\u201d IEEE Transactions on Nuclear Science<\/em>, vol. 60 (3), June 2013, pp. 2222-2231.<\/li>\r\n
  81. Alamaniotis, M., Mattingly, J., & Tsoukalas, L.H., \u201cKernel-based Machine Learning for Background Estimation of NaI Low Count Gamma Ray Spectra,\u201d IEEE Transactions on Nuclear Science<\/em>, vol. 60 (3), June 2013, pp. 2209-2221.<\/li>\r\n
  82. Alamaniotis, M., & Tsoukalas, L.H., \u201cNeuro-SVM Anticipatory System for Online Monitoring of Radiation and Abrupt Change Detection,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global, vol. 1 (2), April-June 2013, pp. 40-53.<\/li>\r\n
  83. Ikonomopoulos, A., Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., \u201cGaussian Processes for State Identification in Pressurized Water Reactors,\u201d Nuclear Technology<\/em>, American Nuclear Society, vol. 182(1), April 2013, pp. 1-12.<\/li>\r\n
  84. McCoy, K., Alamaniotis, M., & Jevremovic, T., \u201cA Conceptual Model for Integrative Monitoring of Nuclear Power Plants Operational Activities based on Historical Nuclear Incidents and Accidents,\u201d International Journal of Monitoring and Surveillance Technologies Research<\/em>, IGI Global, vol. 1 (1), January-March 2013, pp. 69-81.<\/li>\r\n
  85. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cOptimal Assembly of Support Vector Regressors with Application to System Monitoring,\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 27 (6), December 2012, pp. 1250034(1-17).<\/li>\r\n
  86. Chatzidakis, S., Ikonomopoulos, A., & Alamaniotis, M., \u201cAn Algorithmic Approach for RELAP5\/MOD3 Reactivity Insertion Analysis in Research Reactors,\u201d Nuclear Technology<\/em>, American Nuclear Society, vol. 179 (3), September 2012, pp. 392-406.<\/li>\r\n
  87. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cEvolutionary Multiobjective Optimization of Kernel-based Very Short-Term Load Forecasting,\u201d IEEE Transactions on Power Systems<\/em>, vol. 27 (3), August 2012, pp. 1477-1484.<\/li>\r\n
  88. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cProbabilistic Kernel Approach to Online Monitoring of Nuclear Power Plants,\u201d Nuclear Technology<\/em>, American Nuclear Society, vol. 177 (1), January 2012, pp.132-144.<\/li>\r\n
  89. Alamaniotis, M., Ikonomopoulos, A., Jevremovic, T., & Tsoukalas, L.H., \u201cIntelligent Recognition of Signature Patterns in NRF Spectra,\u201d Nuclear Technology<\/em>, American Nuclear Society, vol. 175 (2), August 2011, pp. 480-497.<\/li>\r\n
  90. Alamaniotis, M., Terrill, S., Perry, J., Gao, R., Tsoukalas, L.H., & Jevremovic, T., \u201cA Multisignal Detection of Hazardous Materials for Homeland Security,\u201d Journal of Nuclear Technology and Radiation Protection<\/em>, Vinca Institute, vol. 24 (1), April 2009, pp. 46-55.<\/li>\r\n<\/ol>\r\n

    Book Chapters<\/strong><\/p>\r\n

      \r\n
    1. Alamaniotis, M., \u201cDDDAS-Guided Analysis of Sequential Radiation Measurements Using a Dynamic Matrix Profile Background Model,\u201d Volume-4 of the Dynamic Data Driven Applications Systems Handbook series<\/em>, Springer, pp. 1-15.<\/li>\r\n
    2. Alamaniotis, M., \u201cModeling of Intelligent Control Systems in Nuclear Power Plants,\u201d Handbook on Instrumentation and Control Systems for Nuclear Power Plants<\/em>, Book edited by M. Cappelli, Elsevier, March 2023, Chapter 8, pp. 525-555.<\/li>\r\n
    3. Alamaniotis, M., & Karagiannis, G., \u201cDay Ahead Hourly Solar Power Forecasting using Relevance Vector Regression Models,\u201d Fusion of Machine Learning Paradigms: Theory and Applications<\/em>, Book Edited by. I.K. Hatzilygeroudis, G. Tsihrintzis, and L.C. Jain, Springer, February 2023, Chapter 6, pp. 119-127.<\/li>\r\n
    4. Alamaniotis, M., \u201cIntelligent Data Analytics for Reducing Electricity Consumption in Smart Cities,\u201d Advances in Artificial Intelligence-based Technologies<\/em>, Book Edited by. G. Tsihrintzis, M. Virvou, L. Tsoukalas, A. Esposito and L. Jain, Springer, 2022, Chapter 8, pp. 111-124.<\/li>\r\n
    5. Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H., & Vavalis, E., \u201cDynamic Data Driven Partitioning of Smart Grid for Improving Power Efficiency by combining K-Means and Fuzzy Methods,\u201d Handbook of Dynamic Data Driven Applications Systems<\/em>, Book, edited by E. Blasch et al., 2nd<\/sup> Edition, Springer, May 2022, Chapter 22, pp. 513-535.<\/li>\r\n
    6. Alamaniotis, M., Heifetz, A., \u201cSurvey of Machine Learning Methodologies in Radiation Data Analytics pertained to Nuclear Security,\u201d Advances in Machine\/Deep Learning-based Technologies<\/em>, Book Edited by. G. Tsihrintzis, M. Virvou, L. Tsoukalas, A. Esposito and L. Jain, vol. 23, Springer, 2022, Chapter 6, pp. 97-115.<\/li>\r\n
    7. Alamaniotis, M., \u201cNeuro-Kernel-Machine Network Utilizing Deep Learning and its Application in Predictive Analytics for Smart Cities,\u201d Advances in Data Science: Methodologies and Applications<\/em>, <\/em>Book edited by Gloria Phillips-Wren, Anna Esposito\u00a0and Lakhmi C Jain, Springer: Berlin, 2020, Chapter 14, pp. 293-307.<\/li>\r\n
    8. Alamaniotis, M., & Ktistakis-Papadakis, I., \u201cNeurofuzzy Approach for Control of Smart Appliance for Implementing Demand Response in Price Directed Energy Utilization,\u201d Artificial Intelligence Techniques for a Scalable Energy Transition<\/em>, <\/em>Book edited by M. Sayed-Mouchaweh, Springer: Berlin, 2020, Chapter 10, 2020, pp. 261-278.<\/li>\r\n
    9. Holbrook, L., & Alamaniotis, M., \u201cA Good Defense is a DNN: Defending the IoT with Deep Neural Networks,\u201d Machine Learning Paradigms \u2013 Advances in Theory and Applications of Deep Learning<\/em>, Book edited by G. Tsihrintzis and L. Jain, Springer, 2020, Chapter 6, pp. 125-145.<\/li>\r\n
    10. Alamaniotis, M., \u201cMulti-Kernel Decomposition Paradigm Implementing the Learning from Loads Approach in Smart Power Systems,\u201d Machine Learning Paradigms \u2013 Applications of Learning and Analytics in Intelligent Systems, <\/em>Book edited by G. Tsihrintzis, M. Virvou, E. Sakkopoulos, and L. Jain, vol. 1, Springer: Berlin, 2019, pp. 131-148.<\/li>\r\n
    11. Alamaniotis, M., \u201cData Interpretation and Algorithms,\u201d Active Interrogation in Nuclear Security-Science, Technology, and Systems<\/em>, Book edited by I. Jovanovic and A. Erickson, Springer Nature, 2018, pp. 249-278.<\/li>\r\n
    12. Alamaniotis, M., & Tsoukalas, L.H., \u201cAssessment of Gamma-Ray Spectra Analysis Method Utilizing the Fireworks Algorithm for Various Error Measures,\u201d Critical Developments and Applications of Swarm Intelligence<\/em>, Book edited by Yuhui Shi, IGI-Global, Chapter 7, 2018, pp. 155-181.<\/li>\r\n
    13. Nasiakou, A., Alamaniotis, M., Tsoukalas, L.H., & Vavalis, E., \u201cDynamic Data Driven Partitioning of Smart Grid Using Learning Methods,\u201d Selected Topics on Dynamic Data Driven Application Systems (DDDAS)<\/em>, Edited Book, Springer, 2018.<\/li>\r\n
    14. Alamaniotis, M., & Tsoukalas, L.H., \u201cLearning 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,\u201d Data Analytics and Decision Support for Cybersecurity \u2013 Trends, Methodologies and Applications<\/em>, Book edited by I. Palomares, H.K. Kalutarage and Y. Huang, Springer, 2017, Chapter 8, pp. 223-241.<\/li>\r\n
    15. Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., \u201cData Driven Monitoring of Complex Energy Systems: Gaussian Process Kernel Machines for Fault Identification with Application to Boiling Water Reactors,\u201d Intelligent Computing Systems<\/em>, 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.<\/li>\r\n
    16. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cSwarm Intelligence Optimization: Applications of Particle Swarms in Industrial Engineering and Nuclear Power Plants,\u201d in Computational Intelligence Systems in Industrial Engineerin<\/em>g, Book edited by Cengiz Kahraman, Springer & Atlantis Press, Chapter 9, November 2012, pp. 181-202.<\/li>\r\n<\/ol>\r\n

      Conference\/Workshop Papers<\/strong><\/p>\r\n

        \r\n
      1. Mowen, D., & Alamaniotis, M., \u201cPhysics-Constrained Neural Decoders for Poisson-Distributed Spectral Data: Bridging Nonnegative Matrix Factorization and Deep Learning for Radiation Anomaly Detection,\u201d American Nuclear Society Global 2026<\/em>, Chicago, IL, USA, August 16-20, 2026, pp. 1-10. Accepted<\/strong><\/li>\r\n
      2. Faubel, C., Mowen, D., Miri, M., Alban, U.D., Martinez-Molina, A., & Alamaniotis, M., \u201cFusing Visual and Sensor Data with Multimodal Transformers for Indoor Environmental Quality Prediction in Educational Settings,\u201d Indoor Air Quality, Ventilation and Energy Conservation in Buildings Conference,<\/em> Los Angeles, CA, USA, May 16-18, 2026, pp. 1-6. Accepted<\/strong><\/li>\r\n
      3. Bendle, M., & Alamaniotis, M., \u201cSecurity and Safety Considerations in the Maritime Transport of Nuclear Microreactors: Insights from the Case of a New York\u2013London Route,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      4. Hoverson, K., & Alamaniotis, M., \u201cA Multi-Agent Reinforcement Learning Framework for Autonomous Radiation Source Localization in Complex Environments,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      5. Avlonitis, E., Zhang, X., Alamaniotis, M., & Heifetz, A., \u201cQuantum Hopfield Neural Network Reconstruction of Pulsed Thermography Nondestructive Testing Images,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      6. Schmitt, C., & Alamaniotis, M., \u201cViability of Physics-Informed Neural Networks for Cyber-Physical Defense in Nuclear SMART Valves and Resilient Digital Twins,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      7. Siller, A., & Alamaniotis, M., \u201cIonizing Radiation Modeling using OpenFOAM Frameworks,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      8. Torkornoo, M., Ahmed, S., Gatsis, N., & Alamaniotis, M., \u201cForecast-Informed Load Following for SMR-Powered AI-Data Centers using Fuzzy Supervisory Control,\u201d ANS student conference<\/em>, College Station, TX, USA, April 16-18, 2026, pp. 1-4. Accepted<\/strong><\/li>\r\n
      9. Schmitt, C., Alexiou, M., Chen, M., & Alamaniotis, M., \u201cBenchmarking of Machine Learning Methods in Cybersecurity of SMART Valves against FDI in Nuclear Power Plants,\u201d 40th<\/sup>International Conference on Advanced Information Networking and Applications<\/em>\u00a0(AINA 2026), Wellington, New Zealand, April 8-10, 2026, pp. 1-10. Accepted<\/strong><\/li>\r\n
      10. Zheng, Z., Alamaniotis, M., & Alexiou, M., \u201cUnderstanding Black-Box Adversarial Transferability in Vision Transformer Models,\u201d 40th<\/sup>International Conference on Advanced Information Networking and Applications<\/em>\u00a0(AINA 2026), Wellington, New Zealand, April 8-10, 2026, pp. 1-10. Accepted<\/strong><\/li>\r\n
      11. Aghadinuno, C., Ahmed, S., Alamaniotis, M., Wang, B., & Gatsis, N., \u201cInvestigation of AI Data Center Load Impact on Power System Frequency Using Real World Datasets,\u201d 18th<\/sup> Annual IEEE Green Technologies Conference<\/em>, March 25-27, 2026, Boulder, CO, USA, pp. 1-6. Accepted<\/strong><\/li>\r\n
      12. Chengu S.A., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cA Novel Low-Voltage Ride-Through Control for Bidirectional Megawatt Charging System with Grid Support Capability,\u201d IEEE Applied Power Electronics Conference and Exposition (APEC 2026)<\/em>, San Antonio, TX, USA, March 22-26, 2026, pp. 1-5. Accepted<\/strong><\/li>\r\n
      13. Torkornoo, M., Ahmed, S., Gatsis, N., & Alamaniotis, M., \u201cEnsemble of GPR Models for Very-Short Term Forecasting of AI-Oriented Data Centers Load Demand,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-12. Best Paper Award<\/strong><\/li>\r\n
      14. Avlonitis, E., Alamaniotis, M., Zhang, X., & Heifetz, A., \u201cReconstruction of Blurry Active Thermography Images with Quantum Hopfield Neural Networks,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-11.\u00a0<\/li>\r\n
      15. Mowen, D., & Alamaniotis, M. \u201cA Physics-Informed Transformer with Embedded Nonnegative Matrix Factorization for Gamma-Ray Spectral Anomaly Detection,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-12.\u00a0<\/li>\r\n
      16. Tu-Salaam, A., & Alamaniotis, M. \u201cBenchmarking Deep Learning Architectures for Image-based Surveillance to Improve Maritime Port Security,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-12.\u00a0<\/li>\r\n
      17. Sharma, S., Alamaniotis, M., Noh, J., & Alexiou, M., \u201cResolution Matters: Evaluating Adversarial Robsustness of CNN and ViT Models under Varying Image Resolutions,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-12.\u00a0<\/li>\r\n
      18. Go, J., & Alamaniotis, M. \u201cSimilarity Metric Fuzzy Algorithm for Securing IoT-Connected Radiation Detectors against FDI Attacks,\u201d 14th<\/sup> International Conference on Emerging Internet, Data and Web Technologies<\/em> (EIDWT-2026), Chania, Greece, February 25-27, 2026, pp. 1-12.\u00a0<\/li>\r\n
      19. Torkornoo, M., Ahmed, S., Gatsis, N., & Alamaniotis, M., \u201cVery-Short Term Load Forecasting for AI Data Centers Using Gaussian Process Regression,\u201d IEEE Texas Power and Energy Conference<\/em>, February 8-10, College Station, TX, USA, pp. 1-6.\u00a0<\/li>\r\n
      20. Alamaniotis, M., & Ipiotis, K., \u201cDiscussion on Nonproliferation Issues in Nuclear Maritime Vessels and Solutions offered by Artificial Intelligence,\u201d Advances in Nuclear Nonproliferation Technology and Policy Conference<\/em> (ANTPC 2025), Washington D.C., USA, November 9-13, 2025, pp. 1-4.\u00a0<\/li>\r\n
      21. Bailey, R., & Alamaniotis, M., \u201cInvestigation of Extreme Learning Machines with Application to Real Time Radioisotope Identification,\u201d Advances in Nuclear Nonproliferation Technology and Policy Conference<\/em> (ANTPC 2025), Washington D.C., USA, November 9-13, 2025, pp. 1-4.\u00a0<\/li>\r\n
      22. Avlonitis, E.S., Alamaniotis, M., & Heifetz, A., \u201cPulsed Infrared Thermal Images Reconstruction with Quantum Hopfield Neural Network,\u201d American Nuclear Society Winter Meeting<\/em>, Washington D.C., USA, November 9-13, 2025, pp. 1-3.<\/li>\r\n
      23. Alamaniotis, M., \u201cMulti-RVM Fuzzy based Anticipatory System for Supporting Sustainable Nuclear Microreactor Powered Maritime Ports,\u201d IEEE International Conference on Tools with Artificial Intelligence<\/em>, Athens, Greece, November 3-5, 2025, pp. 1155-1162.<\/li>\r\n
      24. Barba, L., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cReal-Time Charging and Routing for Electric Long-Haul Trucks via Reinforcement Learning,\u201d 2025 IEEE Energy Conversion Conference and Expo<\/em> (ECCE 2025), Philadelphia, PA, USA, October 19-23, 2025, pp. 1-6.<\/li>\r\n
      25. Sun, C., Guo, X., Alamaniotis, M., Guo, Y. & Wang, B., \u201cResNet-1D CNN-based Grid Frequency Estimation,\u201d IEEE PES General Meeting<\/em>, Austin, TX, USA, July 27-31, 2025, pp. 1-5.\u00a0<\/li>\r\n
      26. Alamaniotis, M., \u201cArtificial Intelligence System for Enabling Microreactor-Driven Cold Ironing in Maritime Ports,\u201d IEEE Conference on Artificial Intelligence \u2013 Workshop on Artificial Intelligence for Sustainable Energy<\/em>, San Jose, CA, USA, May 5-7, pp. 1-6.\u00a0<\/li>\r\n
      27. Faubel, P., Alexiou, M., & Alamaniotis, M., \u201cA Preliminary Study on Extending the Apache Spark Framework for FPGA-based Task Offloading,\u201d 16th<\/sup>International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2024), Mytilene, Greece, 9-11 July 2025, pp. 1-7.\u00a0<\/li>\r\n
      28. Mowen, D., & Alamaniotis, M., \u201cThermal Wildlife Image Segmentation using Hybrid Vision Transformers for Smart City Applications,\u201d 16th<\/sup>International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2024), Mytilene, Greece, 9-11 July 2025, pp. 1-7.\u00a0<\/li>\r\n
      29. Katsoudas, P., Alexiou, M., & Alamaniotis, M., \u201cA Preliminary Study on Extending the Apache Spark Framework for FPGA-based Task Offloading,\u201d 16th<\/sup>International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2024), Mytilene, Greece, 9-11 July 2025, pp. 1-7.\u00a0<\/li>\r\n
      30. Loupa, M., Ahmed, S., Alamaniotis, M., & Gatsis, N., \u201cA Stochastic Programming Model for Depot Charging Capacity Assessment of Electric Commercial Fleets Considering Operational Variabilities,\u201d IEEE\/AIAA Transportation Electrification Conference & Electric Aircraft Technologies Symposium<\/em>, Anaheim, CA, USA, June 18-20, 2025, pp. 1-6.\u00a0<\/li>\r\n
      31. Mowen, D., Zhang, Z., Alamaniotis, M., & Heifetz, A., \u201cBenchmarking CNN Architectures for Enhanced Analysis of Pulsed Infrared Thermography Images of Additively Manufactured Metals for Nuclear Applications,\u201d American Nuclear Society Annual Meeting<\/em>, Chicago, IL, USA, June 15-17, 2025, pp. 1-2.\u00a0<\/li>\r\n
      32. Avlonitis, E., Zhang, Z., Alamaniotis, M., & Heifetz, A., \u201cComparative Analysis of Hopfield Neural Network Performance in Processing Pulsed Infrared Thermography Images of Additively Manufactured Metals,\u201d American Nuclear Society Annual Meeting<\/em>, Chicago, IL, USA, June 15-17, 2025, pp. 1-2.\u00a0<\/li>\r\n
      33. Alamaniotis, M., \u201cIntelligent System for enhancing Nuclear Security in the Design of Maritime Ports hosting Nuclear-Powered Ships,\u201d 39th<\/sup> International Conference on Advanced Information Networking and Applications<\/em> (AINA 2025), Barcelona, Spain, April 9-11, 2025, pp. 1-12.\u00a0<\/li>\r\n
      34. Arvanitidis, A., & Alamaniotis, M., \u201cOptimal Security Constrained Economic Dispatch in Nuclear Integrated Energy Systems,\u201d Texas Power and Energy Conference<\/em>, Station College, TX, USA, February 10-11, 2025, pp. 1-6. Best Paper Award<\/b><\/li>\r\n
      35. Barba, L., Ahmed, S., Alamaniotis, M., & Gatsis, N., \u201cDynamic Modeling a nd Optimization of Long-Haul EV Charging Networks,\u201d Texas Power and Energy Conference<\/em>, Station College, TX, USA, February 10-11, 2025, pp. 1-6.<\/li>\r\n
      36. Barba, L., Ahmed, S., Alamaniotis, M., & Gatsis, N., \u201cOptimizing Charging Control for Medium and Heavy-Duty Electric Vehicles Using Deep Reinforcement Learning,\u201d Texas Power and Energy Conference<\/em>, Station College, TX, USA, February 10-11, 2025, pp. 1-6.<\/li>\r\n
      37. Loupa, M., Bentle, M., Ahmed, S., Alamaniotis, M., & Gatsis, N., \u201cAssessing the Impacts of Depot Charging for Medium- and Heavy-Duty Electrified Fleets on the Distribution Grid,\u201d Texas Power and Energy Conference<\/em>, Station College, TX, USA, February 10-11, 2025, pp. 1-6.<\/li>\r\n
      38. Chengu, S.A., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cImproved Grid-Connected Inverter Control for Enhanced Protection in Distribution Systems with High Penetration of Inverter-Based Resources,\u201d Texas Power and Energy Conference<\/em>, Station College, TX, USA, February 10-11, 2025, pp. 1-6.<\/li>\r\n
      39. Alamaniotis, M., \u201cMatrix Profile driven Cross-Structure Modeling of Background Radiation Measurements Applied to Anomaly Detection in Nuclear Security,\u201d American Nuclear Society Winter Conference and Expo<\/em>, Washington D.C., USA, November 17-21, 2024, pp. 1-4.\u00a0<\/li>\r\n
      40. Alamaniotis, , \u201cUtilizing Matrix Profile with the DDDAS Framework for Anomaly Detection in Nuclear Security,\u201d Dynamic Data Driven Applications Systems-2024<\/em>, New Brunswick, NJ, USA, November 6-8, 2024, pp. 1-8.\u00a0<\/li>\r\n
      41. Alamaniotis, M., & Fevgas, A., \u201cIntelligent Management of Integrated Energy Systems in Remote Maritime Environments utilizing Fuzzy Modes,\u201d Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018)<\/em>, Athens, Greece, November 3-6, 2024, pp. 1-6.<\/li>\r\n
      42. Alamaniotis, M., \u201cIntelligent Scheduling of Floating Nuclear Reactor Operation for Implementation of Distributed Smart Energy Systems in Remote Coastal Locations,\u201d 36th<\/sup> IEEE International Conference on Tools with Artificial Intelligence<\/em>, Herndon, VA, USA, October 28-30, 2024, pp. 1-8.\u00a0<\/li>\r\n
      43. Alamaniotis, M., \u201cFuzzy-empowered Decision Making Integrated with DDDAS-Matrix Profile Framework for Anomaly Detection in Radiation Measurements,\u201d 36th<\/sup> IEEE International Conference on Tools with Artificial Intelligence<\/em>, Herndon, VA, USA, October 28-30, 2024, pp. 1-8.\u00a0<\/li>\r\n
      44. Chengu, S.A., Gatsis, N., Alamaniotis, M., Ahmed, S., \u201cHardware-in-the-Loop Testing of the Impact of Grid-Following Inverters Control on Momentary Cessation,\u201d IEEE Energy Conversion Congress and Exposition<\/em>, Phoenix, Arizona, USA, October 20-24, 2024, pp. 1-5.\u00a0<\/li>\r\n
      45. Arvanitidis, A.I, & Alamaniotis, M., \u201cOptimal Economic Dispatch Scheduling in Competitive Energy Market Utilizing a Greedy Q-Learning Algorithm,\u201d IEEE PES Innovative Smart Grid Technologies-Europe<\/em>, October 14-17, 2024, pp. 1-5.\u00a0<\/li>\r\n
      46. Alamaniotis, M., \u201cPreventing a nuclear September 11th<\/sup>: Challenges, Solutions and Concerns in AI-empowered Analysis Software in Sensors,\u201d International Conference on AI-empowered Software Engineering \u2013 JCKBSE<\/em>, Piraeus, Greece, August 27-30, 2024, pp. 1-11.\u00a0<\/li>\r\n
      47. Arvanitidis, A.I, Faubel, C., Martinez-Molina, A., & Alamaniotis, M., \u201c<\/em>Comparative Analysis of Artificial Intelligence Models for HVAC System Optimization in UNESCO Heritage Buildings<\/em>,\u201d<\/em> 15th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2024), Chania, Greece, July 2024, pp. 1-7.<\/li>\r\n
      48. Squire, M., & Alamaniotis, M., \u201cIntelligent Smoothing and Cumulative Sum Control Methods Applied to Signal Peak Detection in Gamma-Ray Measurements,\u201d 15th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2024), Chania, Greece, July 2024, pp. 1-7.<\/li>\r\n
      49. Ramirez, J., Ahmed, S., & Alamaniotis, M., \u201cSpot Price Prediction in Electricity Markets Using an Ensemble of Extreme Learning Machines,\u201d 15th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2024), Chania, Greece, July 2024, pp. 1-4.<\/li>\r\n
      50. Arvanitidis, A.I, & Alamaniotis, M., \u201cQ-Learning Empowered Economic Dispatch for Nuclear-Driven Integrated Energy Systems,\u201d American Nuclear Society Annual Conference<\/em>, June 17-21 2024, pp. 1-4.<\/li>\r\n
      51. Karaiskos, P., Martinez-Molina, A., & Alamaniotis, M., \u201cOptimal insulation and comfort in tiny house design: Balancing energy efficiency and occupant comfort,\u201d 37th<\/sup> PLEA conference<\/em>, Wroclaw, Poland, June 25-28, 2024, pp.1-6.\u00a0<\/em><\/strong><\/li>\r\n
      52. Arvanitidis, A.I, & Alamaniotis, M., \u201cReinforcement Learning-Driven Decision-Making in Deregulated Electricity Markets Involving Greedy Agent-Based Participants,\u201d Texas Power and Energy Conference<\/em>, February 2024, pp. 1-6.<\/li>\r\n
      53. Reyes, A.M., Chengu, A., Gatsis, N., Ahmed, S., & Alamaniotis, M., \u201cModel Explainable AI Method for Fault Detection in Inverter-based Distribution Systems,\u201d Texas Power and Energy Conference<\/em>, February 2024, pp. 1-6.<\/li>\r\n
      54. Chengu, A., Gatsis, N., Alamaniotis, M., & Ahmed, S., \u201cA Novel Fault Ride-Through Scheme for Grid-Forming Inverters under Symmetrical and Asymmetrical Faults in Distribution Systems,\u201d Texas Power and Energy Conference<\/em>, February 2024, pp. 1-6.<\/li>\r\n
      55. Alamaniotis, M., \u201cInvestigation of Computing a Background Radiation Model using self-processed Matrix Profile Method\u201d 2023 ANS Winter Meeting and Technology Expo<\/em>, Washington D.C.<\/em>, USA, November 12-15, November 2023, pp. 405-407.\u00a0<\/li>\r\n
      56. Valdez, L., Alamaniotis, M., & Heifetz, A., \u201cAnomaly Detection in Gamma Source Search Spectra with Hopfield Neural Network on Quantum Computer Simulator,\u201d Advances in Nuclear Nonproliferation Technology and Policy Conference (ANTPC 2023), Washington D.C.<\/em>, USA, November 12-15, 2023, pp. 1.\u00a0<\/li>\r\n
      57. Alamaniotis, M., \u201cThe Chameleon System: A new Approach in Anticipatory based Decision-Making Systems with Application to Electricity Markets,\u201d 35th<\/sup> IEEE International Conference on Tools with Artificial Intelligence<\/em> (ICTAI), Atlanta, GA, USA, November 2-4, 2023, pp. 1-8.\u00a0<\/li>\r\n
      58. Akritidis, L., Fevgas, A., Alamaniotis, M., & Bozanis, P., \u201cConditional Data Synthesis with Deep Generative Models for Imbalanced Dataset Oversampling,\u201d 35th<\/sup> IEEE International Conference on Tools with Artificial Intelligence<\/em> (ICTAI), Atlanta, GA, USA, November 2-4, 2023, pp. 1-8.\u00a0<\/li>\r\n
      59. Alamaniotis, M., \u201cExplainable Prognostics Method through Differential Evolved Ensemble of Relevance Vector Machines,\u201d Annual Conference of the PHM Society, Salt Lake City<\/em>, UT, USA, October 28-November 2, 2023, pp. 1-6.\u00a0<\/li>\r\n
      60. 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), \u00a0Knoxville, TN, USA, July 15-21, 2023, pp. 1-8.<\/li>\r\n
      61. Arvanitidis, A.I., Valdez, L., & Alamaniotis, M., \u201cA Quantum Machine Learning Methodology for Precise Appliance Data Classification in Smart Grids<\/em>,<\/em>\u201d 14th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2023), Volos, Greece, July 2023, pp. 1-6.<\/li>\r\n
      62. Munian, Y., Martinez-Molina, A., Alamaniotis, M., \u201cComparative Analysis of Thermogram and Preprocessed HoG Images using Machine Learning Classifiers,\u201d 14th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2023), Volos, Greece, July 2023, pp. 1-8.<\/li>\r\n
      63. Squire, M., Alamaniotis, M., \u201cSynergism of Fuzzy Numbers and Data Smoothing for Abrupt Change Detection in Gamma-Ray Measurements<\/em>,\u201d 14th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2023), Volos, Greece, July 2023, pp. 1-6.\u00a0<\/em><\/strong><\/li>\r\n
      64. Gharibshahi, E., Miserlis, D., & Alamaniotis, M., \u201cInvestigation of Novel Muon Imaging System in Cardiovascular Operations: A simulation Approach,<\/em>\u201d 14th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2023), Volos, Greece, July 2023, pp. 1-4.\u00a0<\/li>\r\n
      65. Shahabinejad, H., Sudac, D., Alamaniotis, M., Nad, K., & Objodas, J., \u201cBulk Sample Analysis using associated alpha particle neutron generator and artificial neural network,<\/em>\u201d 14th<\/sup>International Conference on Information, Systems and Applications<\/em> (IISA 2023), Volos, Greece, July 2023, pp. 1-4.<\/li>\r\n
      66. Karaiskos, P., Martinez–<\/span>Molina, A., & Alamaniotis, M., \u201cAssessment of Particle Matter Exposure in a Naturally Ventilated Sport Facility and its Potential Impact on COVID-19 Transmission,\u201d ARCC 2023 International Conference<\/em>, Dallas, TX, USA, April 12-15, 2023, pp. 1-6.<\/li>\r\n
      67. Arvanitidis, A.I., Alamaniotis, M., Kontogiannis, D., Vontzos, G., Laitsos, V., & Bargiotas, D., \u201cPerformance Analysis of Single and Multi-Step Short-Term Load Forecasts using Multilayer Perceptron,\u201d The Thirteenth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies<\/em>, Barcelona, Spain, March 13-17, 2023, pp. 1-5. Accepted<\/em><\/li>\r\n
      68. Ayyagari, K.S., Munian, Y., Inupakutika, D., Butukuri, K., Gonzalez, R., & Alamaniotis, M., \u201cSimultaneous Detection and Classification of Dust and Soil on Solar PhotoVoltaic Arrays Connected to a Large Scale Industry: a Case Study,\u201d European Electricity Market Conference<\/em>, Ljubljana, Slovenia, September 13-15, 2022, pp. 1-6.<\/li>\r\n
      69. Gu, S., & Alamaniotis, M., \u201cRadiation Sensor Placement using Reinforcement Learning in Nuclear Security Applications,\u201d 13th<\/sup>\u00a0International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2021), Corfu, Greece, July 2022, pp. 6.<\/li>\r\n
      70. Gonzalez, R., Ahmed, S., & Alamaniotis, M., \u201cDeep Neural Network Based Methodology for Very Short Term Load Residential Load Forecasting,\u201d 13th<\/sup>\u00a0International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2021), Corfu, Greece, July 2022, pp. 6.<\/li>\r\n
      71. Miserlis, D., Munian, Y., Bohannon, W., Wechsler, M., Montero-Baker, M., Ferrer-Cardona, L., Davies, M., Koutakis, P., & Alamaniotis, M., \u201cConvolutional Neural Network Analysis of Tissue Remodeling and Myopathy in Peripheral Arterial Disease,\u201d 13th<\/sup>\u00a0International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2021), Corfu, Greece, July 2022, pp. 8.<\/li>\r\n
      72. Alamaniotis, M., \u201cMulti-Kernel Analysis Method for Intelligent Data Processing with Application to Prediction Making,\u201d 14th<\/sup> International KES Conference \u2013 Intelligent Decision Technologies<\/em>, Rhodes, Greece, June 20-22, 2022, Chapter 25, pp. 279-288.<\/li>\r\n
      73. Squire, M., & Alamaniotis, M., \u201cFuzzy Logic Method for Trend Identification in Radiation Measurements taken with a Mobile Detector with Application to Nuclear Security,\u201d Transaction of the American Nuclear Society Annual Meeting<\/em>, Anaheim, CA, USA, June 12-16, 2022, vol. 126(1), pp. 562-565.<\/li>\r\n
      74. Valdez, L., Alamaniotis, M., & Heifetz, A., \u201cClassical and Quantum Hopfield Network for Application in Radiation Anomaly Detection,\u201d American Nuclear Society Student Conference<\/em>, Urbana-Champaign, IL, USA, April 14-16, 2022, pp. 1-4. BEST PAPER AWARD<\/em><\/li>\r\n
      75. Ibukun, A., Martinez-Molina, A., Nnaji, C., Alamaniotis, M., & Sulbaran, T., \u201cUtility of Wearable Sensing Devices for Environmental Monitoring on Construction Sites,\u201d ASCE Construction Research Congress<\/em>, Arlington, VA, USA, March 9-12, 2022, pp. 10.<\/li>\r\n
      76. Gu, S., & Alamaniotis, M., \u201cRadiation Sensor Placement using Model-Based Reinforcement Learning and Mutual Information,\u201d ANS Winter Meeting and Technology Expo<\/em>, November 30-December 4, 2021, Washington D.C., USA, pp. 3.<\/li>\r\n
      77. Alamaniotis, M., & Heifetz, A., \u201cAn Explainable Artificial Intelligence Approach using a Hopfield Network in Nuclear Security Applications,\u201d ANS Winter Meeting and Technology Expo<\/em>, November 30-December 4, 2021, Washington D.C., USA, pp. 3.<\/li>\r\n
      78. Akritidis, L., Alamaniotis, M., Fevgas, A., & Bozanis, P., \u201cA Scalable Short-Text Clustering Algorithm Using Apache Spark,\u201d 33rd<\/sup> IEEE International Conference on Tools with Artificial Intellingence, <\/em>Virtual Conference, November 9-11, 2021, pp. 1-8.<\/li>\r\n
      79. Nichiforov, C., & Alamaniotis, M., \u201cLoad-based Classification of Academic Buildings using Matrix Profile and Supervised Learning,\u201d IEEE PES Innovative Smart Grid Technologies-Europe<\/em>, October 18-21, 2021, Espoo, Finland, pp. 5.<\/li>\r\n
      80. Gu, S., & Alamaniotis, M., \u201cIrradiation-Driven Dynamic Path-Planning of Moving Airborne Solar Farms Using Reinforcement Learning,\u201d IEEE PES Innovative Smart Grid Technologies-Europe<\/em>, October 18-21, 2021, Espoo, Finland, pp. 5.<\/li>\r\n
      81. Valdez, L., & Alamaniotis, M., \u201cIsotope Recognition in Gamma Spectra by using an Image Driven Hopfield Neural Network,\u201d 2021<\/em> IEEE Nuclear Science Symposium and Medical Imaging Conference<\/em>, Virtual Conference, October 16-23, 2021, pp 2.<\/li>\r\n
      82. Lawrence, J., & Alamaniotis, M., \u201cDevelopment of a Fuzzy Logic Representation Library of Radioisotopes with Application to Nuclear Security,\u201d 2021<\/em> IEEE Nuclear Science Symposium and Medical Imaging Conference<\/em>, Virtual Conference, October 16-23, 2021, pp 2.<\/li>\r\n
      83. Munian, Y., Martinez-Molina, A., Alamaniotis, M., \u201cComparison of Image segmentation, HOG and CNN Techniques for the Animal Detection using Thermography Images in Automobile Applications<\/em>,<\/em>\u201d\u00a012th<\/sup>\u00a0International Conference on Information, Systems and Applications<\/em>\u00a0(IISA 2021), Virtual Conference, July 2021, pp. 8.<\/li>\r\n
      84. Alamaniotis, M., \u201cFuzzy Integration of kernel-based Gaussian Processes applied to Anomaly Detection in Nuclear Security<\/em>,\u201d\u00a09th International Workshop on Combinations of Intelligent Methods and Applications (in conjunction with IISA 2021)<\/em>, Virtual Conference, July 2021, pp. 8.<\/li>\r\n
      85. Alamaniotis, M., Martinez-Molina, A., & Karagiannis, G., \u201cData Driven Update of Load Forecasts in Smart Power Systems using Fuzzy Fusion of Learning GPs,\u201d IEEE PowerTech 2021<\/em>, June 27 \u2013 July 2, 2021, Madrid, Spain, pp. 6. Accepted<\/em><\/li>\r\n
      86. Alamaniotis, M., \u201ciDoubleRad: Cyber-physical System for Enhancing Security of Radiation Measurements in IoT Connected Detectors based on Spectra Comparison using Fuzzy Theil-II Measure,\u201d American Nuclear Society 2021 Annual Meeting<\/em>, June 13-16, 2021, Providence, RI, USA, pp. 4.<\/li>\r\n
      87. Sooby, E., Alamaniotis, M., & Heiftez, A., \u201cGaussian Process Ensemble for Corrosion Modeling and Prediction in Molten Salt Reactors,\u201d 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)<\/em>, June 13-16, 2021, Providence, RI, USA, pp. 8.<\/li>\r\n
      88. Heifetz, A., Bakhtiari, S., Kultgen, D., Huang, X., Sanjie, J., & Alamaniotis, M., \u201cPerspectives on Secure Communications with Advanced Reactors: Ultrasonic and Millimeter Waves Classical and Quantum Communications,\u201d 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)<\/em>, June 13-16, 2021, Providence, RI, USA, pp. 8.\u00a0<\/li>\r\n
      89. Martinez-Molina, A., Williamson, K; I. Owolusi, & Alamaniotis, M., \u201cThe 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,\u201d Building Technology Educators\u2019 Society (BTES) Conference<\/em>, June 10-12, 2021, Auburn, AL, USA, pp.1-8.\u00a0<\/li>\r\n
      90. Munian, Y., Martinez-Molina, A., & Alamaniotis, M., \u201cA Design and Implementation of a Nocturnal Animal Detection Intelligent System in Transportation Applications,\u201d ASCE International Conference on Transportation & Development<\/em>, June 6-9, 2021, Virtual Conference, pp. 12. Accepted<\/em><\/li>\r\n
      91. Hooker, A., Alamaniotis, M., \u201cK-Nearest Neighbor Approach to determine Data Injection Attack on Radiation Sensor in IoT based Security System,\u201d American Nuclear Society 2021 Student Conference<\/em>, April 8-10, 2021, Virtual Meeting, USA, pp. 4.<\/li>\r\n
      92. Alamaniotis, M., and Heifetz, A., \u201cA Machine Learning Approach for Background Radiation Modeling and Anomaly Detection in Radiation Time Series pertained to Nuclear Security,\u201d Winter Meeting and Technology Expo<\/em>, Chicago, IL, USA, November 15-19, 2020, pp. 477-480.<\/li>\r\n
      93. Alamaniotis, M., \u201cIntelligent Data Smoothing of Gamma-Ray Spectra using Relevance Vector Machines with Application to Nuclear Security,\u201d Winter Meeting and Technology Expo<\/em>, Chicago, IL, USA, November 15-19, 2020, pp. 481-483.<\/li>\r\n
      94. Goodman, G., Hirt, Q., Shimizu, C., Papadakis-Ktistakis, I., Alamaniotis, M., & Bourbakis, N., \u201cMethods for Prediction Optimization of the Constrained State-Preserved Extreme Learning Machine,\u201d IEEE International Conference on Tools with Artificial Intelligence 2020, <\/em>November 8-11, 2020, pp. 639-646.<\/li>\r\n
      95. Akritidis, L., Alamaniotis, M., Fevgas, A., & Bozanis, P., \u201cConfronting Sparseness and High Dimensionality in Short Term Clustering via Feature Vector Projections,\u201d IEEE International Conference on Tools with Artificial Intelligence 2020, <\/em>Virtual Conference, November 8-11, 2020, pp. 813-820.<\/li>\r\n
      96. Miserlis, D., Jafari, A., Guda, T., & Alamaniotis, M., \u201cFuzzy Logic Navigation System for Autonomous Endovascular Operations,\u201d 20th<\/sup> IEEE International Conference on Bioinformatics and Bioengineering<\/em>, Virtual Conference, October 26-28, 2020, pp. 865-870.<\/li>\r\n
      97. Lee, Y., & Alamaniotis, M. (2020, October). Unsupervised EEG cybersickness prediction with deep embedded self organizing map. In\u00a02020 IEEE 20th international conference on bioinformatics and bioengineering (BIBE)<\/em>\u00a0(pp. 538-542). IEEE.<\/li>\r\n
      98. Munian, Y., Alamaniotis, M., Martinez-Molina, A., \u201cIntelligent System for Detection of Wild Animals Using HOG and CNN in Automobile Applications,\u201d 9th<\/sup> International Conference on Information, Systems and Applications<\/em> (IISA), Piraeus, Greece, July 2020, pp. 8.\u00a0<\/em><\/strong><\/li>\r\n
      99. Alamaniotis, M., \u201cPredicting Background Count Rate of a Mobile Detector using an optimal ensemble of Learning Kernel Machines,\u201dAmerican Nuclear Society Annual Meeting<\/em>, Virtual Conference, June 7-11, 2020, pp.185-188.<\/li>\r\n
      100. Dimitroulis, P., & Alamaniotis, M., \u201cResidential Energy Management System utilizing Fuzzy Based Decision-Making,\u201d IEEE Texas Power and Energy Conference<\/em>, College Station, TX, USA, February 6-7, 2020, pp. 6.\u00a0<\/li>\r\n
      101. Prakash, V., Fontenot, H., Khan, A., Bing, D., & Alamaniotis, M., \u201cEnsemble Method for Short-Term Load Forecasting Using LSTM, SVR, and FNN and Taking into Account Seasonal Dependency,\u201d <\/strong>ASHRAE Winter Conference and Expo, Orlando, FL, USA, February 1-5, 2020, pp. 1-8.<\/li>\r\n
      102. Campos, B., & Alamaniotis, M., \u201cLessons Learned about Network Defenses of Nuclear Power Plants: A Critical Analysis of Internal Cyber-Attacks,\u201d ANS Winter Meeting and Winter Expo<\/em>, Washington D.C., November 17-21, 2019, vol. 121 (1), pp. 511-514.<\/li>\r\n
      103. Alamaniotis, M., \u201cA Data-Driven Methodology for Estimation of Background Spectrum Utilizing Paired Machine Learning Tools,\u201d ANS Winter Meeting and Winter Expo<\/em>, Washington D.C., November 17-21, 2019, vol 121 (1), pp. 578-581.\u00a0<\/li>\r\n
      104. Holbrook, L., & Alamaniotis, M., \u201cInternet of Things Security Analytics with Deep Learning\u201d 31st<\/sup> IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2019)<\/em>, Portland, OR, USA, pp. 178-185. Best student paper Award<\/em><\/strong><\/li>\r\n
      105. Alamaniotis, M., \u201cELM-Fuzzy Method for Automated Decision Making in Price Directed Electricity Markets,\u201d 16th<\/sup> International Conference on the European Energy Market<\/em>, Ljubljana, Slovenia, September 18-20, 2019, pp. 5.<\/li>\r\n
      106. Alamaniotis, M., \u201cSynergism of Deep Neural Network and ELM for Very Short Term Load Forecasting,\u201d Innovative Smart Grid Technologies Europe<\/em>, Buchurest, Romania, September 29-October 2,2019, pp. 5.<\/li>\r\n
      107. Ayyagari, S.K., Gonzalez, R., Jin, \u03a5., Alamaniotis, M., Ahmed, S., & Gatsis, N., \u201cArtificial Neural Network-Based Voltage Regulation in Distribution Systems using Data-Driven Stochastic Optimization,\u201d IEEE Energy Conversion Congress & Expo, <\/em>Baltimore, MD, USA, September 29-October 3, 2019, pp. 5.<\/li>\r\n
      108. Akritidis, L., Fevgas, A., P. Bozanis, & Alamaniotis, M., \u201cA Self-Pruning Classification Model for News,\u201d 10th<\/sup> International Conference on Information, Intelligence, Systems, and Applications<\/em>, Patras, Greece, July 15-17, 2019, pp.6.<\/li>\r\n
      109. Bhagat, M., Alamaniotis, M., Fevgas, A. \u201cExtreme Interval Electricity Price Forecasting of Wholesale Markets Integrating ELM and Fuzzy Inference,\u201d 10th<\/sup> International Conference on Information, Intelligence, Systems, and Applications<\/em>, Patras, Greece, July 15-17, 2019, pp. 4.<\/li>\r\n
      110. Fevgas, A., Akritidis, L., Alamaniotis, M., Tsompanopoulou, P., & Bozanis, P., \u201cA Study of R-tree Performance in Hybrid Flash\/3DXPoint Storage,<\/em>\u201d 10th<\/sup> International Conference on Information, Intelligence, Systems, and Applications<\/em>, Patras, Greece, July 15-17, 2019, pp. 6.<\/li>\r\n
      111. Alamaniotis, M., & Karagiannis, \u201cMinute Ahead Wind Speed Forecasting Using a Gaussian Process and Fuzzy Driven Assimilation,\u201d IEEE PowerTech<\/em>, Milano, Italy, June 23-27, 2019, pp. 6.<\/li>\r\n
      112. Alamaniotis, M., \u201cFuzzy Data Fusion Utilizing Relevance Vector Machines with Application to Pressurized Water Reactor Monitoring,\u201d 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies<\/em>, Orlando, FL, USA, February 9-14, 2019, pp. 1-7.<\/li>\r\n
      113. Alamaniotis, M., and Ray, A., \u201cA Machine Learning Method Integrating Neural Networks and Learning Gaussian Processes for LOCA Identification in BWRs,\u201d 11th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies<\/em>, Orlando, FL, USA, February 9-14, 2019, pp. 1-8.<\/li>\r\n
      114. Alamaniotis, M., & Karagiannis, G., \u201cLearning Uncertainty of Wind Speed Forecasting using a Fuzzy Multiplexer of Gaussian Processes,\u201d Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018)<\/em>, Dubrovnik, Croatia, November 12-15, 2018, pp. 1-6.<\/li>\r\n
      115. Alamaniotis, M., & Gatsis, N., \u201cEvolutionary Load Morphing in Smart Power System Partitions Ensuring Privacy and Minimizing Cost,\u201d Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (MEDPOWER 2018)<\/em>, Dubrovnik, Croatia, November 12-15, 2018.<\/li>\r\n
      116. Verney-Provatas, A., Alamaniotis, M., Choi C.K., & Tsoukalas, L.H., \u201cA Simulation Platform for Data Generation in Analysis of Detection Algorithms in Radioactive Source Search,\u201d American Nuclear Society Winter Meeting<\/em>, Orlando, FL, USA, November 11-15, 2018, pp. 1-3.<\/li>\r\n
      117. Alamaniotis, M., and Tsoukalas, L.H., \u201cPeak Locating in Gamma-Ray Spectra Using Wavelet Processing and Support Vector Regression with Applications to Nuclear Nonproliferation,\u201d Advances in Nuclear Nonproliferation and Policy Conference<\/em>, Orlando, FL, USA, November 11-15, 2018, pp. 1-4.<\/li>\r\n
      118. Alamaniotis, M., & Papadakis-Ktistakis, I., \u201cFuzzy Leaky Bucket with Application to Coordinating Smart Appliances in Smart Homes,\u201d 30th IEEE International Conference on Tools with Artificial Intelligence<\/em>, Volos, Greece, November 5-7, 2018, pp. 1-6.<\/li>\r\n
      119. Alamaniotis, M., \u201cMorphing to the Mean Approach of Anticipated Electricity Demand in Smart City Partitions using Citizen Elasticities,\u201d IEEE International Smart Cities Conference<\/em>, Kansas City, MS, USA, September 15-17, 2018, pp. 1-7.<\/li>\r\n
      120. Fainti, R., Karasimou, M., Tsionas, I., Tsoukalas, L.H. & Alamaniotis, M., \u201cLoad Management of Electric Vehicles Charging in New Generation Power Markets based on Fuzzy Logic and the Concept of Virtual Budget,\u201d 9th International Conference on Information, Systems and Applications<\/em> (IISA), Zakynthos, Greece, July 2018, pp. 1-7. Invited<\/li>\r\n
      121. Lagari, P., Weidenbenner, S., Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cTesting the sensitivity of a neural based identification algorithm to shielding levels,\u201d American Nuclear Society Annual Meeting<\/em>, Philadelphia, PA, USA, June 2018, pp. 779-782.<\/li>\r\n
      122. Alamaniotis, M., & Karagiannis, G., \u201c’Genetic Driven Multi-Relevance Vector Regression Forecasting of Hourly Wind Speed in Smart Power Systems,\u201d IEEE PES Innovative Smart Grid Technologies \u2013 North America<\/em>, 2018, pp. 1-5.<\/li>\r\n
      123. Alamaniotis, M., & Tsoukalas, L.H., \u201cFuzzy Multi-Kernel Approach in Intelligent Control of Energy Consumption in Smart Cities,\u201d 29th IEEE International Conference on Tools with Artificial Intelligence<\/em> (ICTAI 2017), Boston, MA, USA, November 2017, pp. 1021-1028.<\/li>\r\n
      124. Alamaniotis, M., & Tsoukalas, L.H., \u201cMulti-Kernel Assimilation for Predictive Intervals in Nodal Short-Term Load Forecasting,\u201d IEEE International Conference on Intelligent System Application to Power Systems<\/em> (ISAP 2017), San Antonio, TX, USA, September 2017, pp. 1-6.<\/li>\r\n
      125. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., \u201cThree-Phase Line Overloading Predictive Monitoring utilizing Artificial Neural Networks,\u201d IEEE International Conference on Intelligent System Application to Power Systems<\/em> (ISAP 2017), San Antonio, TX, USA, September 2017, pp. 1-6.<\/li>\r\n
      126. Alamaniotis, M., & Tsoukalas, L.H., \u201cUtilization of Virtual Buffer in Local Area Grids for Electricity Storage in Smart Power Systems,\u201d 49th North American Power Symposium<\/em>, Morgantown, WV, USA, September 2017, pp. 1-6.<\/li>\r\n
      127. Fainti, R., Alamaniotis, M., Tsoukalas, L.H., Karasimou, M., and Tsionas, I., \u201cAmpacity Level Monitoring Utilizing Fuzzy Logic Theory in Deregulated Power Markets,\u201d 8th International Conference on Information, Systems and Applications<\/em> (IISA), Larnaca, Cyprus, August 2017, pp. 1-6.<\/li>\r\n
      128. Nasiakou, A., Alamaniotis, M., Toukalas, L.H., Karagiannis, G., \u201cA Three-Stage Scheme for Consumers\u2019 Partitioning Using Hierarchical Clustering Algorithm,\u201d 8th International Conference on Information, Systems and Applications<\/em> (IISA), Larnaca, Cyprus, August 2017, pp. 1-6.<\/li>\r\n
      129. Bourbakis, N., Alamaniotis, M., & Tsoukalas, L.H., \u201cA Smart Car Model based on Autonomous Agents for Reducing Accidents,\u201d IEEE Transportation Electrification Conference & Expo, Chicago<\/em>, Il, USA, June 22-24, 2017, pp.767-772.<\/li>\r\n
      130. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., \u201cAnticipatory Driven Nodal Electricity Load Morphing in Smart Cities Enhancing Consumption Privacy,\u201d IEEE PES PowerTech Conference<\/em>, Manchester, UK, June 18-22, 2017, pp. 1-6.<\/li>\r\n
      131. Nasiakou, A., Bean, R., & Alamaniotis, M., \u201cDevelopment of Human Machine Interface (HMI) for Digital Control Rooms in Nuclear Power Plants,\u201d 10th International Topical Meeting on Nuclear Power Plant Instrumentation, Control and Human Machine Interface Technologies<\/em> (NPIC & HMIT 2017), San Fracncisco, CA, June 11-15, 2017, pp. 132-141.<\/li>\r\n
      132. Alamaniotis, M., & Tsoukalas, L.H., \u201cAnticipatory System for Detection of Hidden Facilities utilizing Nodal Load Consumption Information in Smart Grids,\u201d IEEE Global Conference on Signal and Information Processing<\/em> (GlobalSip), Washington D.C., USA, December 2016, pp. 806-810.<\/li>\r\n
      133. Alamaniotis, M., Tsoukalas, L.H., & Buckner, M., \u201cPrivacy-Driven Electricity Group Demand Response in Smart Cities Using Particle Swarm Optimization,\u201d IEEE International Conference on Tools with Artificial Intelligence<\/em> (ICTAI 2016), San Jose, CA, USA, November 2016, pp. 946-953.<\/li>\r\n
      134. Belligianni, F., Alamaniotis, M., Fevgas, A., Tsompanopoulou, Bozanis, P., & Tsoukalas, L.H., \u201cAn Internet of Things Architecture for Preserving Privacy of Energy Consumption,\u201d 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion<\/em> (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.<\/li>\r\n
      135. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., \u201cPower Distribution Network Partitioning in Big Data Environment Using K-Means and Fuzzy Logic,\u201d 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion<\/em> (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.<\/li>\r\n
      136. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., \u201cDistribution Congestion Prediction Using Artificial Neural Network for Big Data,\u201d 10th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion<\/em> (Med Power 2016), Belgrade, Serbia, November 6-9, 2016.<\/li>\r\n
      137. Alamaniotis, M., Nasiakou, A., Fainti, R., & Tsoukalas, L.H., \u201cLeaky Bucket Approach Implementing Anticipatory Control for Nodal Power Flow Management in Smart Energy Systems,\u201d IEEE PES Innovative Smart Grid Technologies, Europe<\/em> (ISGT 2016), Ljubljana, Slovenia, October 9-12, 2016, pp. 1-6.<\/li>\r\n
      138. Alamaniotis, M., & Tsoukalas, L.H., \u201cImplementing Smart Energy Systems: Integrating Load and Price Forecasting for Single Parameter based Demand Response,\u201d IEEE PES Innovative Smart Grid Technologies, Europe<\/em> (ISGT 2016), Ljubljana, Slovenia, October 9-12, 2016, pp. 1-6.<\/li>\r\n
      139. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cDeveloping Intelligent Non-proliferation Enabling Capabilities: Very-Short-Term Prediction of Background Radiation in Radioactive Source Search Using Relevance Vector Regression,\u201d Advances in Nuclear Nonproliferation Technology and Policy Conference<\/em> (ANTPC), Santa Fe, NM, September 25-30, 2016, pp. 1-4.<\/li>\r\n
      140. 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., \u201cCNEC and CVT Subcritical Experiments with Category I Special Nuclear Material at the Nevada National Security Site Device Assembly Facility\u201d Institute of Nuclear Materials Annual Conference<\/em>, July 2016, pp. 1-12.<\/li>\r\n
      141. Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., \u201cMatGridGUI- a Toolbox for GridLAB-D Simulation Platform,\u201d 7th International Conference on Information, Intelligence, Systems and Applications<\/em>, Chalkidiki, Greece, July 2016, pp. 1-5.<\/li>\r\n
      142. Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., \u201cThree-Phase Congestion Prediction Utilizing Artificial Neural Networks,\u201d 7th International Conference on Information, Intelligence, Systems and Applications<\/em>, Chalkidiki, Greece, July 2016, pp. 1-6.<\/li>\r\n
      143. Lagari, P.L., Nasiakou, A., Fainti, R., Mao, K., L.H. Tsoukalas, R. Bean & Alamaniotis, M., \u201cEvaluation of Human Machine Interface (HMI) in Nuclear Power Plants with Fuzzy Logic Method,\u201d 7th International Conference on Information, Intelligence, Systems and Applications<\/em>, Chalkidiki, Greece, July 2016, pp. 1-6.<\/li>\r\n
      144. Alamaniotis, M., & Cappelli, M., \u201cReal-Time State Identification of Boiling Water Reactors Using Relevance Vector Machines,\u201d 24th American Society of Mechanical Engineers International Conference on Nuclear Engineering<\/em> (ICONE), Charlotte, NC, USA, June 2016, pp. 8.<\/li>\r\n
      145. Alamaniotis, M., & Tsoukalas, L.H., \u201cMulti-Kernel Anticipatory Approach to Intelligent Control with Application to Load Management of Electrical Appliances,\u201d 16th Mediterranean Conference on Control and Automation<\/em>, Athens, Greece, June 21-24, 2016, pp. 1290-1295.<\/li>\r\n
      146. Lagari, P.L., Mao, K., Tsoukalas, L.H., & Alamaniotis, M., \u201cFuzzy Logic Method for Joint Human Machine Interface Evaluation in Nuclear Power Plants,\u201d ANS Student Conference 2016, American Nuclear Society<\/em>, Madison, Wisconsin, USA, March 31-April 3, 2016, pp.1-4.<\/li>\r\n
      147. Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., \u201cVery-Short Term Forecasting of Electricity Price Signals Using a Pareto Composition of Kernel Machines in Smart Power Systems,\u201d 3rd IEEE Global Conference on Signal and Information Processing<\/em>, Orlando, FL, December 2015, pp. 780-784.<\/li>\r\n
      148. Alamaniotis, M., Tsoukalas, L.H., Fevgas, A., Tsompanopoulou, P., & Bozanis, P., \u201cMultiobjective Unfolding of Shared Power Consumption Pattern using Genetic Algorithm for Estimating Individual Usage in Smart Cities,\u201d 27th International Conference on Tools with Artificial Intelligence<\/em>, Vietri Sul Mare, Italy, November 2015, pp. 398-404.<\/li>\r\n
      149. Alamaniotis, M., & Tsoukalas, L.H., \u201cDeveloping Intelligent Radiation Analysis Systems: A Hybrid Wave-Fuzzy Methodology for Analysis of Radiation Spectra,\u201d 27th International Conference on Tools with Artificial Intelligence<\/em>, Vietri Sul Mare, Italy, November 2015, pp. 1114-1121.<\/li>\r\n
      150. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cShort-Term Gamma Background Anticipation Using Learning Gaussian Processes,\u201d IEEE Nuclear Science Symposium & Medical Imaging Conference Record<\/em>, San Diego, CA, November 2015, pp. 1-4.<\/li>\r\n
      151. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cAnomaly Detection in Radiation Signals Using Kernel Machine Intelligence,\u201d International Conference on Information, Intelligence, Systems and Applications<\/em>, Corfu, Greece, July 2015, pp. 6.<\/li>\r\n
      152. Alamaniotis, M., & Tsoukalas, L.H., \u201cAnticipation of Minutes-Ahead Household Active Power Consumption Using Gaussian Processes,\u201d International Conference on Information, Intelligence, Systems and Applications<\/em>, Corfu, Greece, July 2015, pp. 6.<\/li>\r\n
      153. Bourbakis, N., Ktistakis-Papadakis, I., Tsoukalas, L.H., & Alamaniotis, M., \u201cAn Autonomous Intelligent Wheelchair mounted with Robotic Arms for Smart Homes,\u201d International Conference on Information, Intelligence, Systems and Applications<\/em>, Corfu, Greece, July 2015, pp. 7.<\/li>\r\n
      154. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cData Driven Modeling of Radiation Background using an Ensemble of Learning Methods: Initial Concepts and Preliminary Results,\u201d Transactions of the American Nuclear Society Annual Meeting<\/em>, San Antonio, TX, USA, June 7-11, 2015, pp. 249-252.<\/li>\r\n
      155. Alamaniotis, M., Choi, C., & Tsoukalas, L.H., \u201cA New Approach in Gamma Ray Spectra Analysis: Automated Integration of Peak Detection and Spectrum Fitting using Fuzzy Logic and Multiple Linear Regression,\u201d Transactions of the American Nuclear Society Annual Meeting<\/em>, San Antonio, TX, USA, June 7-11, 2015, pp. 260-263.<\/li>\r\n
      156. Alamaniotis, M., Jin. X., & Ray, A., \u201cOn-line Condition Monitoring of Boiling Water Reactors Using Symbolic Dynamic Analysis,\u201d 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies<\/em> (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 722-732.<\/li>\r\n
      157. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H., \u201cAn Operator\u2019s Support System for Reactor Transients using Fuzzy Logic,\u201d 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies<\/em> (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 2148-2154.<\/li>\r\n
      158. Alamaniotis, M., Tsoukalas, L.H., & Agarwal, V., \u201cPredictive based Monitoring of Nuclear Plant Component Degradation Using Support Vector Regression,\u201d 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies<\/em> (NPIC&HMIT 2015), American Nuclear Society, Charlotte, NC, USA, February 2015, pp. 1199-1207.<\/li>\r\n
      159. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H., \u201cA Bayesian Approach to Monitoring Spent Fuel Using Cosmic Ray Muons,\u201d American Nuclear Society Winter Meeting and Nuclear Technology Expo<\/em>, November 9-13, 2014, Anaheim, CA, USA, pp. 369-370.<\/li>\r\n
      160. Alamaniotis, M., & Tsoukalas, L.H., \u201cIntegration of Price Anticipation and Self-Elasticity for Hour-Ahead Electricity Bidding and Purchasing,\u201d 9th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion:<\/em> (MEDPOWER 2014), November 2014, Piraeus, Greece, pp. 1-4.<\/li>\r\n
      161. Alamaniotis, M., Chatzidakis, S., & Tsoukalas, L.H., \u201cMonthly Load Forecasting Using Gaussian Process Regression,\u201d 9th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion<\/em> (MEDPOWER 2014), November 2014, Piraeus, Greece, pp. 1-7.<\/li>\r\n
      162. Alamaniotis, M., Tsoukalas, L.H.., & Bourbakis, N., \u201cVirtual Cost Approach: Electricity Consumption Scheduling in Smart Grids for Price Directed Electricity Markets,\u201d 5th International Conference on Information, Intelligence, Systems and Applications<\/em>, July 2014, Chania, Greece, pp. 38-43.<\/li>\r\n
      163. Alamaniotis, M., Agarwal, V., & Jevremovic, T., \u201cAnticipatory Monitoring and Control of Complex Energy Systems Using a Fuzzy based Fusion of Support Vector Regressors,\u201d 5th International Conference on Information, Intelligence, Systems and Applications<\/em>, July 2014, Chania, Greece, pp. 33-37.<\/li>\r\n
      164. Chatzidakis, S., Alamaniotis, M., & Tsoukalas, L.H. \u201cCreep Rupture Forecasting for high Performance Energy Systems,\u201d 5th International Conference on Information, Intelligence, Systems and Applications<\/em>, July 2014, Chania, Greece, pp. 95-99. Best Paper Award<\/strong><\/li>\r\n
      165. Alamaniotis, M., Hernandez, H., & Jevremovic, T., \u201cRole of Nuclear Forensics defined as a Digital Problem with Neurofuzzy Approach in various Applications,\u201d American Institute of Chemical Engineers Annual Conference 2013<\/em> (AIChE 2013), San Fransisco, CA, USA November 2013, pp. 1-7.<\/li>\r\n
      166. Alamaniotis, M., & Tsoukalas, L., \u201cLayered based Approach to Virtual Storage for Smart Power Systems,\u201d 4th International Conference on Information, Intelligence, Systems and Applications<\/em>, July 2013, Piraeus, Greece, pp. 22-27.<\/li>\r\n
      167. Alamaniotis, M., Hernandez, H., & Jevremovic, T., \u201cApplication of Support Vector Regression in Removing Poisson Fluctuation from Pulse Height Gamma-Ray Spectra,\u201d 4th International Conference on Information, Intelligence, Systems and Applications<\/em>, July 2013, Piraeus, Greece, pp. 18-21.<\/li>\r\n
      168. Owen, L., Alamaniotis, M., & Jevremovic, T., \u201cAutomated Signal-Layered Algorithm for Processing Complex Gamma Radiation Spectra,\u201d American Nuclear Society Student Conference<\/em>, April 2013, Boston, MA, USA, pp. 1-5. In Best 4 Conference Papers<\/strong><\/li>\r\n
      169. Painter, D., Alamaniotis, M., & Jevremovic, T., \u201cAnalysis of City Grown Bing Cherry Trees using Neutron Activation Analysis,\u201d American Nuclear Society Student Conference<\/em>, April 2013, Boston, MA, USA, pp.1-5.<\/li>\r\n
      170. Santora, J., Alamaniotis, M., & Jevremovic, T., \u201cViticulture Improvement Applicable to all Plants,\u201d American Nuclear Society Student Conference<\/em>, April 2013, Boston, MA, USA, pp. 1-5.<\/li>\r\n
      171. Alamaniotis, M., Ikonomopoulos, A., Alamaniotis, A., Bargiotas, D., & Tsoukalas, L.H., \u201cDay-ahead Electricity Price Forecasting using Optimized Multiple-Regression of Relevance Vector Machines,\u201d 8th Mediterranean Conference on Power Generation, Transmission, Distribution, and Energy Conversion (<\/em>MEDPOWER 2012), October 2012, Cagliari, Italy, pp. 1-6.<\/li>\r\n
      172. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H., \u201cBackground Spectrum Estimation for Low Count Spectra Using Kernel-Modeled Gaussian Processes,\u201d American Nuclear Society Annual Meeting<\/em>, June 2012, Chicago, IL, USA, pp. 273-274.<\/li>\r\n
      173. Alamaniotis, M., Heifetz, A., Raptis, A., & Tsoukalas, L.H., \u201cFuzzy Logic Radio Isotope Identifier for Gamma Spectra Analysis in Source Search Applications,\u201d American Nuclear Society Annual Meeting<\/em>, June 2012, Chicago, IL, USA, pp. 211-212.<\/li>\r\n
      174. Young, J., Alamaniotis, M., Gao, R., & Tsoukalas, L.H., \u201cDevelopment of Path Search Toolkit for Nuclear Non-Proliferation Applications,\u201d American Nuclear Society Annual Meeting<\/em>, June 2012, Chicago, IL, USA, pp. 205-206.<\/li>\r\n
      175. Young, J, Alamaniotis, M., & Tsoukalas, L.H., \u201cFuzzy Logic Detection of Special Nuclear Materials in Aqueous Environments,\u201d American Nuclear Society Student Conference<\/em>, Las Vegas, NV, April 2012, pp. 1-2.<\/li>\r\n
      176. Alamaniotis, M., Ikonomopoulos, A., Gao, R., & Tsoukalas, L.H., \u201cLessons learned in Accidents: An Intelligent Systems Perspective for Nuclear Power Plant Safety,\u201d American Nuclear Society Winter Meeting<\/em>, Washington D.C., 2011, pp. 305.<\/li>\r\n
      177. Alamaniotis, M., Ikonomopoulos., & Tsoukalas, L.H., \u201cA Pareto Optimization Approach of a Gaussian Process Ensemble for Short-Term Load Forecasting,\u201d International Conference on Intelligent System Applications on Power Systems<\/em> (ISAP 2011), Crete, Greece, September 2011, pp. 48(1-6).<\/li>\r\n
      178. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cOnline Surveillance of Nuclear Power Plant Peripheral Components using Support Vector Regression,\u201d International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems<\/em> (ICI 2011), Daejeon, Korea, August 2011, pp. 1230(1-6).<\/li>\r\n
      179. Alamaniotis, M., Xiao, S., Young, J., Gao, R., Tsoukalas, L.H., Choe, D., & Jevremovic T., \u201cUsing iMASS to simulate the Tracking\/Movement of Special Nuclear Materials,\u201d American Institute of Chemical Engineers Annual Conference<\/em> (AIChE 2010), Salt Lake City, UT, November 2010, pp. 1-6.<\/li>\r\n
      180. Alamaniotis, M., Gao, R., & Tsoukalas, L.H., \u201cTowards an Energy Internet: A Game-Theoretic Approach to Price-Directed Energy Utilization,\u201d 1st International ICST Conference on E-Energy<\/em>, Athens, Greece, October 2010, pp. 3-10.<\/li>\r\n
      181. Alamaniotis, M., Ikonomopoulos, A., & Tsoukalas, L.H., \u201cDistributed System for Operator Support in Nuclear Power Plants,\u201d 1st International Conference for Undergraduate and Postgraduate Students in Computer Engineering, Informatics, related Technologies and Applications: EUREKA 2010<\/em>, Patras, Greece, October 2010, pp. 1-9.<\/li>\r\n
      182. Alamaniotis, M., Tsoukalas, L.H., & Ikonomopoulos, A., \u201cAutomated System for Plan Realization in Nuclear Power Plants,\u201d European Safety and Reliability Conference 2010<\/em> (ESREL 2010), Rhodes, Greece, September 2010, pp. 2103-2109.<\/li>\r\n
      183. Alamaniotis, M., Ikonomopoulos, A. & Tsoukalas, L.H., \u201cGaussian Processes for Failure Prediction of Slow Degradation Components in Nuclear Power Plants\u201d, European Safety and Reliability Conference<\/em> (ESREL 2010), Rhodes, Greece, September 2010, pp. 2096-2102.<\/li>\r\n
      184. Alamaniotis, M., Young, J., Tsoukalas, L.H., & Jevremovic, T., \u201cAssessment of Wavelet Processing in Removing Background Peaks from NRF Spectra,\u201d American Nuclear Society (ANS) Student Conference<\/em>, Ann Arbor, MI, April 2010, pp. 1-2.<\/li>\r\n
      185. Alamaniotis, M., Young, J., Tsoukalas, L.H., & Jevremovic, T., \u201cAn Insight in Wavelet Denoising of Nuclear Resonance Spectra for Identification of Hazardous Materials,\u201d 1st National Conference on Advanced Tools and Solutions for Nuclear Material Detection<\/em>, Salt Lake City, UT, March 2010, pp. 1-6.<\/li>\r\n
      186. Alamaniotis, M., Gao, R., Tsoukalas, L.H., & Jevremovic, T., \u201cExpert System for Decision Making and Instructing Nuclear Resonance Fluorescence Cargo Interrogation,\u201d 21st IEEE International Conference on Tools with Artificial Intelligence<\/em>, Newark, NJ, November 2009, pp. 666-673.<\/li>\r\n
      187. Alamaniotis, M., Gao, R., Tsoukalas, L.H., & Jevremovic, T., \u201cIntelligent Order-based Method for Synthesis of NRF Spectra and Detection of Hazardous Materials,\u201d 21st IEEE International Conference on Tools with Artificial Intelligence<\/em>, Newark, NJ, November 2009, pp. 658-665.<\/li>\r\n
      188. Alamaniotis, M., Young, J., Perry, J., Xiao, S., Agarwal, V., Forsberg, P., Gao, R., Choi, C., 4Tsoukalas, L.H., & Jevremovic, T., \u201cEngineering Solution to Nuclear Material Detection at Ports: Introducing the Novel iMASS Paradigm,\u201d 21st IEEE International Conference on Tools with Artificial Intelligence<\/em>, Newark, NJ, November 2009, pp. 679-682.<\/li>\r\n
      189. Pantelopoulos, A., Alamaniotis, M., Jevremovic, T., Park, M.S., Chung, M.S., & Bourbakis N., \u201cLG-Graph based Detection of NRF Signatures: Initial Results and Comparison,\u201d 21st IEEE International Conference on Tools with Artificial Intelligence<\/em>, Newark, NJ, November 2009, pp. 683-686.<\/li>\r\n
      190. Alamaniotis, M., Youtsos, M., Gao, R., & Tsoukalas L.H., \u201cPseudo Neural Network based Diagnostic System for Two Phase Annular Flow in Nuclear Power Plants,\u201d International Conference on Optimization Using Exergy-based Methods and Computational Fluid Dynamics<\/em>, Berlin, Germany, October 2009, pp. 203-208.<\/li>\r\n
      191. Alamaniotis, M., Gao, R., & Tsoukalas, L.H., \u201cDistributed Intelligence System for Online Action Taking in Non-Anticipated Situations in Nuclear Power Plants,\u201d ICAPS-2009 Scheduling and Planning Applications Workshop<\/em>, Thessaloniki, Greece, September 2009, pp. 7-13.<\/li>\r\n
      192. Alamaniotis, M., Gao, R., Jevremovic, T., & Tsoukalas, L.H., \u201cIntelligent Detection of SNM in Liquid Containers,\u201d 16th International Conference on Systems, Signals and Image Processing<\/em>, Chalkida, Greece, June 2009, pp. 1-4.<\/li>\r\n
      193. Alamaniotis, M., Terrill, S., Gao, R., & Jevremovic, T., \u201cAutomated Multisignal Detection of Special Nuclear Material in Cargo Containers,\u201d American Nuclear Society (ANS) Student Conference<\/em>, Gainesville, Florida, April 2009, pp. 1-2. Best Paper Award<\/strong><\/li>\r\n
      194. Pantelopoulos, A., Alamaniotis, M., Bourbakis, N., & Jevremovic, T., \u201cHeuristic Identification of Nuclear Materials from NRF Spectra\u201d, American Nuclear Society (ANS) Student Conference<\/em>, Gainesville, Florida, April 2009, pp. 1-2.<\/li>\r\n<\/ol>\r\n

        Abstracts in Journals<\/strong><\/p>\r\n

          \r\n
        1. Miserlis, D., Munian, Y., Cardona, L.M.F., Teixeira, P.G., DuBose, J.J., Davies, M.G., Bohannon, W., Koutakis, P., & Alamaniotis, M., \u201cBenchmarking EfficientNetB7, InceptionResNetV2, InceptionV3, and xception artificial neural networks applications for aortic pathologies analysis,\u201d\u00a0Journal of Vascular Surgery<\/em>,\u00a0vol. 77(6), 2023, p.e345.<\/li>\r\n
        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., \u201cEvaluating The Diagnostic Ability Of Six Different Artificial Neural Networks From The Subcellular Microenvironment To The Clinical Manifestation,\u201d\u00a0Arteriosclerosis, Thrombosis, and Vascular Biology<\/em>,\u00a0vol. 43(Suppl_1), 2023, pp.A544-A544.<\/li>\r\n<\/ol>\r\n

          Technical Reports<\/strong><\/p>\r\n

            \r\n
          1. Valdez, L., Alamaniotis, M., & Heifetz, A., \u201cAnomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover\u2019s Algorithm on a Quantum Computing Simulator,\u201d Argonne National Laboratory<\/em>, ANL\/NSE-22\/78, September 2022, pp. 1-15.<\/li>\r\n<\/ol>\r\n

            Newsletter Articles<\/em><\/strong><\/p>\r\n

              \r\n
            1. Alamaniotis, M., \u201cVision of Smart Cities as a means for Implementing Smart Nuclear Security,\u201d IEEE Smart Cities Newsletter, <\/em>January 2023, pp. 19-24.<\/li>\r\n<\/ol>\r\n

              Book Reviews<\/em><\/strong><\/p>\r\n

                \r\n
              1. Alamaniotis, M., Review of \u201cHandbook on Artificial Intelligence-Empowered Applied Software Engineering, Vol. 1-2\u201d by Virvou, M., Tsihrintzis, G.A., Bourbakis, N.G., Jain, L.C., Intelligent Decision Technologies: An International Journal<\/em>, vol.X(X), March 2023, pp. 1-2. In Press<\/li>\r\n<\/ol>\r\n

                Editorials<\/strong><\/p>\r\n

                  \r\n
                1. Pan, S., & Alamaniotis, M., \u201cSpecial Issue on Selected Papers from the 32nd<\/sup> Annual Conference on Tools with Artificial Intelligence (ICTAI 2020),\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 31 (7), November 2022, pp. (2202007) 1.<\/li>\r\n
                2. Alamaniotis, M., \u201cMessage from the IEEE ICTAI 2020 General Chair,\u201d 32nd<\/sup> International Conference on Tools with Artificial Intelligence<\/em> (ICTAI 2020), November 2020, pp. xxvii.<\/li>\r\n
                3. Alamaniotis, M., \u201cSpecial Issue on Selected Papers from the 30th<\/sup> Annual Conference on Tools with Artificial Intelligence (ICTAI 2018),\u201d International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 26 (5), June 2019, pp. (#2002002)1-1.<\/li>\r\n
                4. Alamaniotis, M., \u201cMessage from the Program Chair,\u201d 30th<\/sup> International Conference on Tools with Artificial Intelligence<\/em> (ICTAI 2018), November 2018, pp. 22-22.<\/li>\r\n
                5. Bourbakis, N., & Alamaniotis, M., \u201cMessage from the Applications of AI in Smart Cities Track Chairs,\u201d 30th<\/sup> International Conference on Tools with Artificial Intelligence<\/em> (ICTAI 2018), November 2018, pp. 24-24.<\/li>\r\n
                6. Mali, A., & Alamaniotis, M., \u201cSpecial Issue from ICTAI 2016\u201d, International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 26 (5), October 2017, pp. (#1702004)1-1.<\/li>\r\n
                7. Mali, A., & Alamaniotis, M., \u201cSpecial Issue from ICTAI 2016\u201d, International Journal on Artificial Intelligence Tools<\/em>, World Scientific Publishing Company, vol. 26 (5), October 2017, pp. (#1702004)1-1.<\/li>\r\n<\/ol>\r\n","protected":false},"excerpt":{"rendered":"

                  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,\u00a0 and viii) Editorials Journal Papers De Leon, R., & Alamaniotis, M. \u201cLocal-Global Graph Framework for Gamma Signature Spectrum Identification Supporting Interpretable Decisions in Nuclear Security,\u201d Intelligent Decision Technologies, Sage, … <\/p>\n

                  Continue reading “Publications”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":87,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-14","page","type-page","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nPublications - Dr. Miltos Alamaniotis<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ceid.utsa.edu\/malamaniotis\/publications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Publications - Dr. Miltos Alamaniotis\" \/>\n<meta property=\"og:description\" content=\"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,\u00a0 and viii) Editorials Journal Papers De Leon, R., & Alamaniotis, M. \u201cLocal-Global Graph Framework for Gamma Signature Spectrum Identification Supporting Interpretable Decisions in Nuclear Security,\u201d Intelligent Decision Technologies, Sage, … Continue reading "Publications"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ceid.utsa.edu\/malamaniotis\/publications\/\" \/>\n<meta property=\"og:site_name\" content=\"Dr. Miltos Alamaniotis\" \/>\n<meta property=\"article:modified_time\" content=\"2026-04-04T05:50:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ceid.utsa.edu\/malamaniotis\/wp-content\/uploads\/sites\/72\/2017\/07\/utsa-orange-bar.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"48 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/ceid.utsa.edu\\\/malamaniotis\\\/publications\\\/\",\"url\":\"https:\\\/\\\/ceid.utsa.edu\\\/malamaniotis\\\/publications\\\/\",\"name\":\"Publications - 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