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Dr. Vassilis Kaburlasos
International Hellenic University (IHU)

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Proceedings
Published: 25 June 2021 in Engineering Proceedings
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This work represents any distribution of data by an Intervals’ Number (IN), hence it represents all-order data statistics, using a “small” number of L intervals. The INs considered are induced from images of grapes that ripen. The objective is the accurate prediction of grape maturity. Based on an established algebra of INs, an optimizable IN-regressor is proposed, implementable on a neural architecture, toward predicting future INs from past INs. A recursive scheme tests the capacity of the IN-regressor to learn the physical “law” that generates the non-stationary time-series of INs. Computational experiments demonstrate comparatively the effectiveness of the proposed techniques.

ACS Style

Christos Bazinas; Eleni Vrochidou; Chris Lytridis; Vassilis Kaburlasos. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Engineering Proceedings 2021, 5, 12 .

AMA Style

Christos Bazinas, Eleni Vrochidou, Chris Lytridis, Vassilis Kaburlasos. Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach. Engineering Proceedings. 2021; 5 (1):12.

Chicago/Turabian Style

Christos Bazinas; Eleni Vrochidou; Chris Lytridis; Vassilis Kaburlasos. 2021. "Time-Series of Distributions Forecasting in Agricultural Applications: An Intervals’ Numbers Approach." Engineering Proceedings 5, no. 1: 12.

Research article
Published: 17 June 2021 in Mathematical Problems in Engineering
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The task of child engagement estimation when interacting with a social robot during a special educational procedure is studied. A multimodal machine learning-based methodology for estimating the engagement of the children with learning difficulties, participating in appropriate designed educational scenarios, is proposed. For this purpose, visual and audio data are gathered during the child-robot interaction and processed towards deciding an engaged state of the child or not. Six single and three ensemble machine learning models are examined for their accuracy in providing confident decisions on in-house developed data. The conducted experiments revealed that, using multimodal data and the AdaBoost Decision Tree ensemble model, the children’s engagement can be estimated with 93.33% accuracy. Moreover, an important outcome of this study is the need for explicitly defining the different engagement meanings for each scenario. The results are very promising and put ahead of the research for closed-loop human centric special education activities using social robots.

ACS Style

George A. Papakostas; George K. Sidiropoulos; Chris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Efi Kourampa; Elpida Karageorgiou; Petros Kechayas; Maria T. Papadopoulou. Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning. Mathematical Problems in Engineering 2021, 2021, 1 -10.

AMA Style

George A. Papakostas, George K. Sidiropoulos, Chris Lytridis, Christos Bazinas, Vassilis G. Kaburlasos, Efi Kourampa, Elpida Karageorgiou, Petros Kechayas, Maria T. Papadopoulou. Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning. Mathematical Problems in Engineering. 2021; 2021 ():1-10.

Chicago/Turabian Style

George A. Papakostas; George K. Sidiropoulos; Chris Lytridis; Christos Bazinas; Vassilis G. Kaburlasos; Efi Kourampa; Elpida Karageorgiou; Petros Kechayas; Maria T. Papadopoulou. 2021. "Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning." Mathematical Problems in Engineering 2021, no. : 1-10.

Review
Published: 10 June 2021 in Electronics
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In recent years, social robots have become part of a variety of human activities, especially in applications involving children, e.g., entertainment, education, companionship. The interest of this work lies in the interaction of social robots with children in the field of special education. This paper seeks to present a systematic review of the use of robots in special education, with the ultimate goal of highlighting the degree of integration of robots in this field worldwide. This work aims to explore the technologies of robots that are applied according to the impairment type of children. The study showed a large number of attempts to apply social robots to the special education of children with various impairments, especially in recent years, as well as a wide variety of social robots from the market involved in such activities. The main conclusion of this work is the finding that the specific field of application of social robots is at the first development step; however, it is expected to be of great concern to the research community in the coming years.

ACS Style

George Papakostas; George Sidiropoulos; Cristina Papadopoulou; Eleni Vrochidou; Vassilis Kaburlasos; Maria Papadopoulou; Vasiliki Holeva; Vasiliki-Aliki Nikopoulou; Nikolaos Dalivigkas. Social Robots in Special Education: A Systematic Review. Electronics 2021, 10, 1398 .

AMA Style

George Papakostas, George Sidiropoulos, Cristina Papadopoulou, Eleni Vrochidou, Vassilis Kaburlasos, Maria Papadopoulou, Vasiliki Holeva, Vasiliki-Aliki Nikopoulou, Nikolaos Dalivigkas. Social Robots in Special Education: A Systematic Review. Electronics. 2021; 10 (12):1398.

Chicago/Turabian Style

George Papakostas; George Sidiropoulos; Cristina Papadopoulou; Eleni Vrochidou; Vassilis Kaburlasos; Maria Papadopoulou; Vasiliki Holeva; Vasiliki-Aliki Nikopoulou; Nikolaos Dalivigkas. 2021. "Social Robots in Special Education: A Systematic Review." Electronics 10, no. 12: 1398.

Journal article
Published: 25 May 2021 in Computers and Electronics in Agriculture
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Automation of grapevine agricultural tasks, e.g., harvesting, requires reliable methods for detecting the exact cutting points of the grape bunches. Dynamically changing vineyard environments, differences between plant varieties, illumination, occlusion, color similarities, and varying contrast make the detection of the grapes’ stems in unstructured environments difficult. In this work, a grape stem detection methodology in images is proposed, towards introducing an autonomous grape harvesting robot (ARG), as an affordable and consistent alternative to the time-consuming specialized work of an experienced harvester. For this purpose, a regression convolutional neural network (RegCNN) is applied for executing a stem segmentation task. Twelve Convolutional Neural Network (CNN) model architectures derived by the combination of three different feature learning sub-networks with four meta-architectures, are investigated. For the first time, stem detection is tackled as a regression problem in a way to alleviate the imbalanced data phenomenon that may occur in vineyard images. In order to justify the effectiveness of the RegCNN models, the same CNN architectures are tested in a typical classification (ClaCNN) setup. Comparative results involving two datasets with different characteristics reveal that the regression models outperform the classification ones. Grape bunches stems are detected with an Intersection-over-Union (IU) performance of up to 98.18% with RegCNNs, before post-processing optimization. Moreover, by applying a Genetic Algorithm (GA)-based parameter tuning mechanism, optimized post-processing parameters lead to an improved IU accuracy of up to 98.90% for the UNET_MOBILENETV2 model with acceptable real-time performance. Compared to other similar methodologies, the proposed method provides higher correct stem detection rates in unconstrained and highly changing environments, e.g., vineyards, and thus it is appropriate for robust real-time stem identification towards facilitating the agricultural tasks executed by a robot harvester.

ACS Style

Τheofanis Kalampokas; Εleni Vrochidou; George A. Papakostas; Theodore Pachidis; Vassilis G. Kaburlasos. Grape stem detection using regression convolutional neural networks. Computers and Electronics in Agriculture 2021, 186, 106220 .

AMA Style

Τheofanis Kalampokas, Εleni Vrochidou, George A. Papakostas, Theodore Pachidis, Vassilis G. Kaburlasos. Grape stem detection using regression convolutional neural networks. Computers and Electronics in Agriculture. 2021; 186 ():106220.

Chicago/Turabian Style

Τheofanis Kalampokas; Εleni Vrochidou; George A. Papakostas; Theodore Pachidis; Vassilis G. Kaburlasos. 2021. "Grape stem detection using regression convolutional neural networks." Computers and Electronics in Agriculture 186, no. : 106220.

Journal article
Published: 09 May 2021 in Mathematics
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Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.

ACS Style

Eleni Vrochidou; Chris Lytridis; Christos Bazinas; George Papakostas; Hiroaki Wagatsuma; Vassilis Kaburlasos. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics 2021, 9, 1063 .

AMA Style

Eleni Vrochidou, Chris Lytridis, Christos Bazinas, George Papakostas, Hiroaki Wagatsuma, Vassilis Kaburlasos. Brain Signals Classification Based on Fuzzy Lattice Reasoning. Mathematics. 2021; 9 (9):1063.

Chicago/Turabian Style

Eleni Vrochidou; Chris Lytridis; Christos Bazinas; George Papakostas; Hiroaki Wagatsuma; Vassilis Kaburlasos. 2021. "Brain Signals Classification Based on Fuzzy Lattice Reasoning." Mathematics 9, no. 9: 1063.

Journal article
Published: 29 April 2021 in Electronics
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This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards.

ACS Style

Eleni Vrochidou; Konstantinos Tziridis; Alexandros Nikolaou; Theofanis Kalampokas; George Papakostas; Theodore Pachidis; Spyridon Mamalis; Stefanos Koundouras; Vassilis Kaburlasos. An Autonomous Grape-Harvester Robot: Integrated System Architecture. Electronics 2021, 10, 1056 .

AMA Style

Eleni Vrochidou, Konstantinos Tziridis, Alexandros Nikolaou, Theofanis Kalampokas, George Papakostas, Theodore Pachidis, Spyridon Mamalis, Stefanos Koundouras, Vassilis Kaburlasos. An Autonomous Grape-Harvester Robot: Integrated System Architecture. Electronics. 2021; 10 (9):1056.

Chicago/Turabian Style

Eleni Vrochidou; Konstantinos Tziridis; Alexandros Nikolaou; Theofanis Kalampokas; George Papakostas; Theodore Pachidis; Spyridon Mamalis; Stefanos Koundouras; Vassilis Kaburlasos. 2021. "An Autonomous Grape-Harvester Robot: Integrated System Architecture." Electronics 10, no. 9: 1056.

Article
Published: 13 October 2020 in International Journal of Technology and Design Education
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This paper presents the results of the survey that was conducted during 2018 in four countries: Bulgaria, Greece, Bosnia and Herzegovina and Croatia. The survey is a part of activities within the project “Increasing the well being of the population by RObotic and ICT based iNNovative education” (RONNI), funded by the Danube Strategic Project Fund (DSPF). The survey included two target groups: the teachers/experts and the parents; and the corresponding questionnaires (QR) were delivered to schools in each of the participating countries. A total of 428 subjects participated in the survey (231 parents and 197 teachers/experts). Seven hypotheses related to stakeholders attitudes and opinions were formed and tested in the work, showing highly favorable sentiment toward inclusion of robotics and information technology (IT) in the classroom but with some exceptions. The conclusions drawn, based on the analysis of the results, can be used for proposing strategies and methodologies aimed at boosting inclusion of IT in the teaching process, transferable across the regions to support effective learning as well as to identify possible problems with their implementation in relation to attitudes of stakeholders: teachers and parents.

ACS Style

Josip Musić; Mirjana Bonković; Stanko Kružić; Tea Marasović; Vladan Papić; Snezhana Kostova; Maya Dimitrova; Svetoslava Saeva; Milen Zamfirov; Vassilis Kaburlasos; Eleni Vrochidou; George Papakostas; Theodore Pachidis. Robotics and information technologies in education: four countries from Alpe-Adria-Danube Region survey. International Journal of Technology and Design Education 2020, 1 -23.

AMA Style

Josip Musić, Mirjana Bonković, Stanko Kružić, Tea Marasović, Vladan Papić, Snezhana Kostova, Maya Dimitrova, Svetoslava Saeva, Milen Zamfirov, Vassilis Kaburlasos, Eleni Vrochidou, George Papakostas, Theodore Pachidis. Robotics and information technologies in education: four countries from Alpe-Adria-Danube Region survey. International Journal of Technology and Design Education. 2020; ():1-23.

Chicago/Turabian Style

Josip Musić; Mirjana Bonković; Stanko Kružić; Tea Marasović; Vladan Papić; Snezhana Kostova; Maya Dimitrova; Svetoslava Saeva; Milen Zamfirov; Vassilis Kaburlasos; Eleni Vrochidou; George Papakostas; Theodore Pachidis. 2020. "Robotics and information technologies in education: four countries from Alpe-Adria-Danube Region survey." International Journal of Technology and Design Education , no. : 1-23.

Journal article
Published: 23 June 2020 in Electronics
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The outbreak of the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV2) has resulted in a significant disruption of almost all aspects of everyday life. Several governments around the world have adopted emergency actions to reduce spreading of the virus, which included suspension of non-essential activities and the implementation of social distancing practices. In our case, governmental measures have resulted in the suspension of our experimental protocol for testing the effectiveness of robot-based treatment of children diagnosed with Autism Spectrum Disorder (ASD) compared to conventional human (therapist)-based treatment. These circumstances led to an investigation of the potential of tele-consulting. This paper describes alternatives to implement synchronous and asynchronous therapeutic sessions for children already participating in the protocol, in order to reduce the negative effects of the strict cessation of the in-person sessions. The usefulness of our approach was assessed by recording the children’s and the parent’s satisfaction via questionnaires. In addition, we compare satisfaction between the synchronous and asynchronous sessions. The results show that the approach has been very satisfactory and useful for both children and parents, and that this was especially the case for the robot-based material.

ACS Style

Chris Lytridis; Christos Bazinas; George Sidiropoulos; George A. Papakostas; Vassilis G. Kaburlasos; Vasiliki-Aliki Nikopoulou; Vasiliki Holeva; Athanasios Evangeliou. Distance Special Education Delivery by Social Robots. Electronics 2020, 9, 1034 .

AMA Style

Chris Lytridis, Christos Bazinas, George Sidiropoulos, George A. Papakostas, Vassilis G. Kaburlasos, Vasiliki-Aliki Nikopoulou, Vasiliki Holeva, Athanasios Evangeliou. Distance Special Education Delivery by Social Robots. Electronics. 2020; 9 (6):1034.

Chicago/Turabian Style

Chris Lytridis; Christos Bazinas; George Sidiropoulos; George A. Papakostas; Vassilis G. Kaburlasos; Vasiliki-Aliki Nikopoulou; Vasiliki Holeva; Athanasios Evangeliou. 2020. "Distance Special Education Delivery by Social Robots." Electronics 9, no. 6: 1034.

Journal article
Published: 13 March 2020 in Mathematics
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Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

ACS Style

Chris Lytridis; Anna Lekova; Christos Bazinas; Michail Manios; Vassilis G. Kaburlasos. WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications. Mathematics 2020, 8, 413 .

AMA Style

Chris Lytridis, Anna Lekova, Christos Bazinas, Michail Manios, Vassilis G. Kaburlasos. WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications. Mathematics. 2020; 8 (3):413.

Chicago/Turabian Style

Chris Lytridis; Anna Lekova; Christos Bazinas; Michail Manios; Vassilis G. Kaburlasos. 2020. "WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications." Mathematics 8, no. 3: 413.

Review
Published: 07 December 2019 in Journal of Imaging
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Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

ACS Style

Efthimia Mavridou; Eleni Vrochidou; George A. Papakostas; Theodore Pachidis; Vassilis G. Kaburlasos. Machine Vision Systems in Precision Agriculture for Crop Farming. Journal of Imaging 2019, 5, 89 .

AMA Style

Efthimia Mavridou, Eleni Vrochidou, George A. Papakostas, Theodore Pachidis, Vassilis G. Kaburlasos. Machine Vision Systems in Precision Agriculture for Crop Farming. Journal of Imaging. 2019; 5 (12):89.

Chicago/Turabian Style

Efthimia Mavridou; Eleni Vrochidou; George A. Papakostas; Theodore Pachidis; Vassilis G. Kaburlasos. 2019. "Machine Vision Systems in Precision Agriculture for Crop Farming." Journal of Imaging 5, no. 12: 89.

Conference paper
Published: 17 November 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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The effectiveness of social robots in education is typically demonstrated, circumstantially, involving small samples of students [1]. Our interest here is in special education in Greece regarding Autism Spectrum Disorder (ASD) involving large samples of children students. Following a recent work review, this paper reports the specifications of a protocol for testing the effectiveness of robot (NAO)-based treatment of ASD children compared to conventional human (therapist)-based treatment. The proposed protocol has been developed by the collaboration of a clinical scientific team with a technical scientific team. The modular structure of the aforementioned protocol allows for implementing parametrically a number of tools and/or theories such as the theory-of-mind account from psychology; moreover, the engagement of the innovative Lattice Computing (LC) information processing paradigm is considered here toward making the robot more autonomous. This paper focuses on the methodological and design details of the proposed intervention protocol that is underway; the corresponding results will be reported in a future publication.

ACS Style

Vasiliki Holeva; Vasiliki-Aliki Nikopoulou; Maria Papadopoulou; Eleni Vrochidou; George A. Papakostas; Vassilis G. Kaburlasos. Toward Robot-Assisted Psychosocial Intervention for Children with Autism Spectrum Disorder (ASD). Transactions on Petri Nets and Other Models of Concurrency XV 2019, 484 -493.

AMA Style

Vasiliki Holeva, Vasiliki-Aliki Nikopoulou, Maria Papadopoulou, Eleni Vrochidou, George A. Papakostas, Vassilis G. Kaburlasos. Toward Robot-Assisted Psychosocial Intervention for Children with Autism Spectrum Disorder (ASD). Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():484-493.

Chicago/Turabian Style

Vasiliki Holeva; Vasiliki-Aliki Nikopoulou; Maria Papadopoulou; Eleni Vrochidou; George A. Papakostas; Vassilis G. Kaburlasos. 2019. "Toward Robot-Assisted Psychosocial Intervention for Children with Autism Spectrum Disorder (ASD)." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 484-493.

Conference paper
Published: 01 July 2018 in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Watermarking regards the insertion of an "invisible" pattern to an image. It is known that watermarking can be pursued by modulating the coefficients of image moments, the latter are computationally extracted features; moreover, previous work has employed a Fuzzy Inference System (FIS) with triangular membership functions in order to compute each moment coefficient. This work proposes using irregular membership functions of multi-parametric Intervals' Numbers (INs) instead. Practical advantages of the proposed method are demonstrated comparatively in preliminary human-machine interaction applications of a Social Robot. The proposed techniques are presented as a case-specific study of modeling in Cyber-Physical Systems (CPSs) using Lattice Computing (LC) techniques.

ACS Style

George A. Papakostas; Vassilis G. Kaburlasos. Modeling in Cyber-Physical Systems by Lattice Computing Techniques: The Case of Image Watermarking Based on Intervals’ Numbers. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2018, 1 -6.

AMA Style

George A. Papakostas, Vassilis G. Kaburlasos. Modeling in Cyber-Physical Systems by Lattice Computing Techniques: The Case of Image Watermarking Based on Intervals’ Numbers. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2018; ():1-6.

Chicago/Turabian Style

George A. Papakostas; Vassilis G. Kaburlasos. 2018. "Modeling in Cyber-Physical Systems by Lattice Computing Techniques: The Case of Image Watermarking Based on Intervals’ Numbers." 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 1-6.

Conference paper
Published: 01 August 2015 in 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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This paper introduces a preliminary extension of the Fuzzy Cognitive Map (FCM) architecture based on Lattice Computing (LC) techniques namely Linguistic Fuzzy Cognitive Maps (LFCM). The proposed LFCM is able to handle large scale data in pattern classification applications. This enhancement is achieved by applying a novel data meta-representation, defined in a mathematical lattice, including several advantages. Based on this mechanism a new FCM classifier model is constructed and its performance is studied herein. Preliminary experimental results are both promising and competitive. Future work extensions are discussed.

ACS Style

G.A. Papakostas; Elpiniki Papageorgiou; V.G. Kaburlasos. Linguistic Fuzzy Cognitive Map (LFCM) for pattern recognition. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2015, 1 -7.

AMA Style

G.A. Papakostas, Elpiniki Papageorgiou, V.G. Kaburlasos. Linguistic Fuzzy Cognitive Map (LFCM) for pattern recognition. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2015; ():1-7.

Chicago/Turabian Style

G.A. Papakostas; Elpiniki Papageorgiou; V.G. Kaburlasos. 2015. "Linguistic Fuzzy Cognitive Map (LFCM) for pattern recognition." 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 1-7.

Journal article
Published: 16 July 2015 in IEEE Computational Intelligence Magazine
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This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice. In particular, the interest here focuses on lattices of (Type-1) Intervals' Numbers (INs), where an IN represents a distribution of image features including orthogonal moments. A neural classifier, namely fuzzy lattice reasoning (flr) fuzzy-ARTMAP (FAM), or flrFAM for short, is described for learning distributions of INs; hence, Type-2 INs emerge. Four benchmark image pattern recognition applications are demonstrated. The results obtained by the proposed techniques compare well with the results obtained by alternative methods from the literature. Furthermore, due to the isomorphism between the lattice of INs and the lattice of fuzzy numbers, the proposed techniques are straightforward applicable to Type-1 and/or Type-2 fuzzy systems. The far-reaching potential for deep learning in big data applications is also discussed.

ACS Style

Vassilis Kaburlasos; George A. Papakostas. Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications. IEEE Computational Intelligence Magazine 2015, 10, 42 -51.

AMA Style

Vassilis Kaburlasos, George A. Papakostas. Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications. IEEE Computational Intelligence Magazine. 2015; 10 (3):42-51.

Chicago/Turabian Style

Vassilis Kaburlasos; George A. Papakostas. 2015. "Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications." IEEE Computational Intelligence Magazine 10, no. 3: 42-51.

Journal article
Published: 02 October 2014 in Neurocomputing
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We present a Computer Assisted Diagnosis (CAD) system for Alzheimer’s disease (AD). The proposed CAD system employs MRI data features and applies a Lattice Computing (LC) scheme. To this end feature extraction methods are adopted from the literature, toward distinguishing healthy people from Alzheimer diseased ones. Computer assisted diagnosis is pursued by a k-NN classifier in the LC context by handling this task from two different perspectives. First, it performs dimensionality reduction over the high dimensional feature vectors and, second it classifies the subjects inside the lattice space by generating adaptively class boundaries. Computational experiments using a benchmark MRI dataset regarding AD patients demonstrate that the proposed classifier performs well comparatively to state-of-the-art classification models.

ACS Style

G.A. Papakostas; A. Savio; M. Graña; V.G. Kaburlasos. A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing 2014, 150, 37 -42.

AMA Style

G.A. Papakostas, A. Savio, M. Graña, V.G. Kaburlasos. A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing. 2014; 150 ():37-42.

Chicago/Turabian Style

G.A. Papakostas; A. Savio; M. Graña; V.G. Kaburlasos. 2014. "A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data." Neurocomputing 150, no. : 37-42.

Journal article
Published: 01 October 2014 in Engineering Applications of Artificial Intelligence
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ACS Style

Yazdan Jamshidi; Vassilis G. Kaburlasos. gsaINknn: A GSA optimized, lattice computing knn classifier. Engineering Applications of Artificial Intelligence 2014, 35, 277 -285.

AMA Style

Yazdan Jamshidi, Vassilis G. Kaburlasos. gsaINknn: A GSA optimized, lattice computing knn classifier. Engineering Applications of Artificial Intelligence. 2014; 35 ():277-285.

Chicago/Turabian Style

Yazdan Jamshidi; Vassilis G. Kaburlasos. 2014. "gsaINknn: A GSA optimized, lattice computing knn classifier." Engineering Applications of Artificial Intelligence 35, no. : 277-285.

Conference paper
Published: 01 July 2014 in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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This paper compares two alternative feature data meta-representations using Intervals' Numbers (INs) in the context of the Minimum Distance Classifier (MDC) model. The first IN meta-representation employs one IN per feature vector, whereas the second IN meta-representation employs one IN per feature per class. Comparative classification experiments with the standard minimum distance classifier (MDC) on two benchmark classification problems, regarding face/facial expression recognition, demonstrate the superiority of the aforementioned second IN meta-representation. This superiority is attributed to an IN's capacity to represent discriminative, all-order data statistics in a population of features.

ACS Style

George A. Papakostas; Vassilis G. Kaburlasos. Lattice computing (LC) meta-representation for pattern classification. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2014, 39 -44.

AMA Style

George A. Papakostas, Vassilis G. Kaburlasos. Lattice computing (LC) meta-representation for pattern classification. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2014; ():39-44.

Chicago/Turabian Style

George A. Papakostas; Vassilis G. Kaburlasos. 2014. "Lattice computing (LC) meta-representation for pattern classification." 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 39-44.

Conference paper
Published: 01 July 2014 in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Recent work has proposed an enhancement of Formal Concept Analysis (FCA) in a tunable, hybrid formal context including both numerical and nominal data [1]. This work introduces FCknn, that is a granular knn classifier based on hybrid concepts, whose effectiveness is demonstrated on benchmark datasets from the literature including both numerical and nominal data. Preliminary experimental results compare well with the results by alternative classifiers from the literature. Formal concepts are interpreted as descriptive decision-making knowledge (rules) induced from the data.

ACS Style

Vassilis G. Kaburlasos; Vassilis Tsoukalas; Lefteris Moussiades. FCknn: A granular knn classifier based on formal concepts. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2014, 61 -68.

AMA Style

Vassilis G. Kaburlasos, Vassilis Tsoukalas, Lefteris Moussiades. FCknn: A granular knn classifier based on formal concepts. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2014; ():61-68.

Chicago/Turabian Style

Vassilis G. Kaburlasos; Vassilis Tsoukalas; Lefteris Moussiades. 2014. "FCknn: A granular knn classifier based on formal concepts." 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 61-68.

Journal article
Published: 01 April 2014 in Journal of Engineering Science and Technology Review
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V. G. Kaburlasos; L. Moussiades. Induction of formal concepts by lattice computing techniques for tunable classification. Journal of Engineering Science and Technology Review 2014, 7, 1 -8.

AMA Style

V. G. Kaburlasos, L. Moussiades. Induction of formal concepts by lattice computing techniques for tunable classification. Journal of Engineering Science and Technology Review. 2014; 7 (1):1-8.

Chicago/Turabian Style

V. G. Kaburlasos; L. Moussiades. 2014. "Induction of formal concepts by lattice computing techniques for tunable classification." Journal of Engineering Science and Technology Review 7, no. 1: 1-8.

Journal article
Published: 01 March 2014 in Information Fusion
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Vassilis Kaburlasos; Theodore Pachidis. A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application. Information Fusion 2014, 16, 68 -83.

AMA Style

Vassilis Kaburlasos, Theodore Pachidis. A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application. Information Fusion. 2014; 16 ():68-83.

Chicago/Turabian Style

Vassilis Kaburlasos; Theodore Pachidis. 2014. "A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application." Information Fusion 16, no. : 68-83.