MPhil-Computer Science Department-International Hellenic University

Papers

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52. 51. Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants

N.-I. Galanis, P. Vafiadis, K.-G. Mirzaev, and G. A. Papakostas, “Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants,” Algorithms, vol. 15, no. 11, p. 391, 2022.

50. Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

S. Tsimenidis, E. Vrochidou, and G.A. Papakostas, “Omics Data and Data Representations for Deep Learning-Based Predictive Modeling,” International Journal of Molecular Sciences, vol. 23, no. 20, p. 12272, 2022.

49. How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)

T. V. Maliamanis, K. D. Apostolidis, and G. A. Papakostas, “How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA),” Biomedicines, vol. 10, no. 10, p. 2545, Oct. 2022.

48. Variable Selection on Reflectance NIR Spectra for the Prediction of TSS in Intact Berries of Thompson Seedless Grapes

C. Chariskou, E. Vrochidou, A. J. Daniels, and V.G. Kaburlasos, “Variable Selection on Reflectance NIR Spectra for the Prediction of TSS in Intact Berries of Thompson Seedless Grapes,” Agronomy, vol. 12, no. 9, p. 2113, 2022.

47. Biometrics: Going 3D

G.G. Samatas and G.A. Papakostas, “Biometrics: Going 3D,” Sensors, vol. 22, no. 17, p. 6364, 2022.

46. An update on cooking recipe generation with Machine Learning and Natural Language Processing

N.-I. Galanis and G.A. Papakostas, "An update on cooking recipe generation with Machine Learning and Natural Language Processing," 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), pp. 739-744, 2022.

45. Visual Quality Inspection of Pomegranate Crop Using a Novel Dataset and Deep Learning

A. Koufatzis, E. Vrochidou and G.A. Papakostas, "Visual Quality Inspection of Pomegranate Crop Using a Novel Dataset and Deep Learning," 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1-4, 2022.

44. On 3D Reconstruction Using RGB-D Cameras

Κ.Α. Tychola, Ι. Tsimperidis and G.A. Papakostas, "On 3D Reconstruction Using RGB-D Cameras," Digital, vol. 2, no. 4, pp. 401-423, 2022.

43. A Survey on Deep Learning for Building Load Forecasting

I. Patsakos, E. Vrochidou and G.A. Papakostas, "A Survey on Deep Learning for Building Load Forecasting," Mathematical Problems in Engineering, vol. 2022, Article ID 1008491, 2022.

42. Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

K.D. Apostolidis and G.A. Papakostas, “Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning,” Journal of Imaging, vol. 8, no. 6, p. 155, 2022,

41. Text Classification Using Intuitionistic Fuzzy Set Measures—An Evaluation Study

G.K. Sidiropoulos, N. Diamianos, K.D. Apostolidis, and G.A. Papakostas, “Text Classification Using Intuitionistic Fuzzy Set Measures—An Evaluation Study,” Information, vol. 13, no. 5, p. 235, 2022

40. Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces

S.S. Moumgiakmas and G.A. Papakostas, “Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces,” Computers, vol. 11, no. 5, p. 61, Apr. 2022.

39. Inertial Measurement Units (IMUs) in Mobile Robots over the Last Five Years: A Review

G.G. Samatas and T.P. Pachidis, "Inertial Measurement Units (IMUs) in Mobile Robots over the Last Five Years: A Review," Designs, vol. 6, no. 1, p.17, 2022.

38. Fsmpy: A Fuzzy Set Measures Python Library

G.K. Sidiropoulos, K.D. Apostolidis, N. Damianos, and G.A. Papakostas, “Fsmpy: A Fuzzy Set Measures Python Library,” Information, vol. 13, no. 2, p. 64, 2022.

37. Computer Vision in Self-Steering Tractors

E. Vrochidou, D. Oustadakis, A. Kefalas, and G. A. Papakostas, “Computer Vision in Self-Steering Tractors,” Machines, vol. 10, no. 2, p. 129, 2022.

36. The Use of Blockchain Technology in e-Government Services

I. Lykidis, G. Drosatos, and K. Rantos, “The Use of Blockchain Technology in e-Government Services,” Computers, vol. 10, no. 12, p. 168, 2021.

35. Computer Vision for Astronomical Image Analysis

S. Karypidou, I. Georgousis and G.A. Papakostas, "Computer Vision for Astronomical Image Analysis," 2021 IEEE International Conference on Progress in Informatics and Computing (PIC), 2021, pp. 94-101.

34. Evaluating Convolutional Neural Networks for No -Reference Image Quality Assessment

K.D. Apostolidis, T. Polyzos, I. Grigoriadis and G.A. Papakostas, "Evaluating Convolutional Neural Networks for No -Reference Image Quality Assessment," 2021 4th International Conference on Signal Processing and Information Security (ICSPIS), pp. 68-71, 2021.

33. Refined Color Texture Classification Using CNN and Local Binary Pattern

K.M. Hosny, T. Magdy, N.A. Lashin, K. Apostolidis and G.A. Papakostas, "Refined Color Texture Classification Using CNN and Local Binary Pattern," Mathematical Problems in Engineering, vol. 2021, Article ID 5567489, 2021.

32. Deep learning in IoT intrusion detection

S. Tsimenidis, T. Lagkas and K. Rantos, "Deep Learning in IoT Intrusion Detection," Journal of Network and Systems Management, vol. 30, no. 8, 2022,

31. Privacy-preserving solutions in blockchain-enabled internet of vehicles

K. Kaltakis, P. Polyzi, G. Drosatos, K. Rantos, "Privacy-preserving solutions in blockchain-enabled internet of vehicles," Applied Sciences, vol. 11, no. 21, p. 9792, 2021.

30. A study on text classification for applications in special education

L. C. Karathanasi, C. Bazinas, G. Iordanou, V. G. Kaburlasos, “A study on text classification for applications in special education”, 29th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2021), pp. 1-5, Hvar, Croatia, 23-25 September 2021.

29. A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

K. D. Apostolidis and G. A. Papakostas, “A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis,” Electronics, vol. 10, no. 17, p. 2132, Sep. 2021.

28. LIA: A Mathbot Assistant for First-grade Students

L. Moussiades and G. Zografos, "LIA: A Mathbot Assistant for First-grade Students," European Journal of Engineering and Technology Research. 6, 5 (Aug. 2021), 89-93, 2021.

27. Computer Vision for Fire Detection on UAVs—From Software to Hardware

S.S. Moumgiakmas, G.G. Samatas and G.A. Papakostas, "Computer Vision for Fire Detection on UAVs—From Software to Hardware," Future Internet, vol. 13, no. 8, p. 200, 2021.

26. Moment Transform-Based Compressive Sensing in Image Processing

T. Kalampokas and G.A. Papakostas, "Moment Transform-Based Compressive Sensing in Image Processing," 28th International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava-Slovakia, 2-4 June 2021.

25. Machine Learning Meets Natural Language Processing - The story so far

N.-I. Galanis, P. Vafiadis, K.-G. Mirzaev, G.A. Papakostas, "Machine Learning Meets Natural Language Processing - The story so far," In: Maglogiannis I., Macintyre J., Iliadis L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham, 25 – 27 June, 2021.

24. Feature Extraction for Finger-Vein-Based Identity Recognition

G.K. Sidiropoulos, P. Kiratsa, P. Chatzipetrou, G.A. Papakostas, "Feature Extraction for Finger-Vein-Based Identity Recognition," Journal of Imaging 7 (5), 89, 2021.

23. Remote Crop Sensing with IoT and AI on the Edge

P. Savvidis and G.A. Papakostas, "Remote Crop Sensing with IoT and AI on the Edge," 2021 IEEE World AI IoT Congress (AIIoT), pp. 0048-0054, 2021.

22. Predictive Maintenance - Bridging Artificial Intelligence and IoT

G.G. Samatas, S.S. Moumgiakmas, G.A. Papakostas, "Predictive Maintenance - Bridging Artificial Intelligence and IoT," 2021 IEEE World AI IoT Congress (AIIoT), pp. 0413-0419, 2021.

21. Machine Biometrics - Towards Identifying Machines in a Smart City Environment

G.K. Sidiropoulos and G.A. Papakostas, "Machine Biometrics - Towards Identifying Machines in a Smart City Environment," 2021 IEEE World AI IoT Congress (AIIoT), pp. 0197-0201, 2021.

20. New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix

K.M. Hosny, S.T. Kamal, M.M. Darwish, G.A. Papakostas, "New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix," Electronics 10 (9), 1066, 2021.

19. Machine Learning Vulnerability in Medical Imaging

T.V. Maliamanis and G.A. Papakostas, “Machine Learning Vulnerability in Medical Imaging”, in Machine Learning, Big Data, and IoT for Medical Informatics, P. Kumar, Y. Kumar, M.A. Tawhid (Eds.), Elsevier, pp. 1-18, 2021.

18. Machine learning for cloud resources management - An overview

V.N. Tsakalidou, P. Mitsou, G.A. Papakostas, "Machine learning for cloud resources management - An overview," International Conference on Expert Clouds and Applications (ICOECA 2021), 2021.

17. EEG Signal Analysis for Seizure Detection Using Recurrence Plots and Tchebichef Moments

K. Tziridis, T. Kalampokas, G.A. Papakostas, "EEG Signal Analysis for Seizure Detection Using Recurrence Plots and Tchebichef Moments," IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021. (Best Paper in Biomedical Computing, Bioinformatics)

16. Privacy-Preserving Blockchain-Based Solutions in the Internet of Things

N. Zapoglou, I. Patsakos, G. Drosatos, and K. Rantos, "Privacy-Preserving Blockchain-Based Solutions in the Internet of Things," EAI International Conference on Cognitive Computing and Cyber Physical Systems (IC4S 2020), Cyberspace, 2020.

15. Crowd simulation for crisis management: the outcomes of the last decade

G. Sidiropoulos, K. Chairi, and L. Moussiades, “Crowd simulation for crisis management: the outcomes of the last decade,” Machine Learning with Applications, vol. 2, pp. 100009, 2020.

14. Performance Evaluation of Convolutional Neural Networks for Gait Recognition

K. Apostolidis, P. Amanatidis and G.A. Papakostas, "Performance Evaluation of Convolutional Neural Networks for Gait Recognition," 24th Pan-Hellenic Conference on Informatics (PCI), Athens, Greece, 2020.

13. Chatbots: History, technology, and applications

E. Adamopoulou and L. Moussiades, "Chatbots: History, technology, and applications", Machine Learning with Applications, vol. 2, pp.100006, 2020.

12. DOME-T: Adversarial computer vision attack on deep learning models based on Tchebichef image moments

T. Maliamanis and G.A. Papakostas, "DOME-T: Adversarial computer vision attack on deep learning models based on Tchebichef image moments," 13th International Conference on Machine Vision (ICMV), Rome, Italy, 2020.

11. An Industrial IoT Edge Node for Buffer Level Detection in a Cardboard Production Line

G. Avramidis and D. Karampatzakis, "An Industrial IoT Edge Node for Buffer Level Detection in a Cardboard Production Line," International Scientific Conference of Communications, Information, Electronic and Energy Systems (CIEES 2020), Borovets, Bulgaria, 2020.

10. Single Sample Face Recognition Using Convolutional Neural Networks for Automated Attendance Systems

F. Filippidou and G.A. Papakostas, "Single Sample Face Recognition Using Convolutional Neural Networks for Automated Attendance Systems," 4th International Conference on Intelligent Computing in Data Sciences (ICDS2020), Fez, Morocco, 2020.

9. Metis: Multi-Agent Based Crisis Simulation System

G. Sidiropoulos, C. Kiourt, L. Moussiades, "Metis: Multi-Agent Based Crisis Simulation System", 11th EETN Conference on Artificial Intelligence (SETN 2020), 2020.

8. Image Transmission via LoRa Networks – A Survey

A. Staikopoulos, V. Kanakaris and G. A. Papakostas, "Image Transmission via LoRa Networks – A Survey," 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), Beijing -China, pp. 150-154, 2020.

7. Evaluation of LBP Variants in Retinal Blood Vessels Segmentation Using Machine Learning

E. Badeka, C.I. Papadopoulou and G.A. Papakostas, "Evaluation of LBP Variants in Retinal Blood Vessels Segmentation Using Machine Learning", 4th IEEE International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1-7, Fez-Morocco, 2020.

6. Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

F. Filippidou, L. Moussiades, "Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems", Artificial Intelligence Applications and Innovations (AIAI), pp. 73-82, Chalkidiki-Greece, 2020.

5. An Overview of Chatbot Technology

E. Adamopoulou, L. Moussiades, "An Overview of Chatbot Technology", Artificial Intelligence Applications and Innovations (AIAI), pp. 373-383, Chalkidiki-Greece, 2020.

4. Adversarial computer vision: a current snapshot

T. Maliamanis, G.A. Papakostas, "Adversarial computer vision: a current snapshot", 12th International Conference on Machine Vision (ICMV), vol. 11433, pp. 1143328, Amsterdam-Netherlands, 2019.

3. Gender identification through facebook data analysis using machine learning techniques

P.I. Kiratsa, G.K. Sidiropoulos, E.V. Badeka, C.I. Papadopoulou, A.P. Nikolaou, G.A. Papakostas, "Gender identification through facebook data analysis using machine learning techniques", 22nd Pan-Hellenic Conference on Informatics (PCI), pp. 117-120, Athens-Greece, 2018.

2. Improving Management of Logistics By Integrating Blockchain, IoT and Drones Towards Wellbeing

V. Kanakaris, A. Staikopoulos, A. Sourmelis and G.A. Papakostas, "Improving Management of Logistics By Integrating Blockchain, IoT and Drones Towards Wellbeing", 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), September 13-15, Split – Supetar, Croatia, 2018.

1. Internet of Things (IoT): A Way to Improve Human Wellbeing

A. Sourmelis, A. Staikopoulos, V. Kanakaris and G.A. Papakostas, "Internet of Things (IoT): A Way to Improve Human Wellbeing", 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), September 13-15, Split – Supetar, Croatia, 2018.