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Dr. Charis Ntakolia
National Technical University of Athens

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0 Image Processing
0 Mathematical Programming
0 Numerical Analysis
0 Optimization Algorithms
0 Path Planning

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Route Planning
Mathematical Programming

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Journal article
Published: 09 May 2021 in Computers & Operations Research
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Personalized tourist route planning (TRP) and navigation are online or real-time applications whose mathematical modeling leads to complex optimization problems. These problems are usually formulated with mathematical programming and can be described as NP hard problems. Moreover, the state-of-the-art (SOA) path search algorithms do not perform efficiently in solving multi-objective optimization (MO) problems making them inappropriate for real-time processing. To address the above limitations and the need for online processing, a swarm intelligence graph-based pathfinding algorithm (SIGPA) for MO route planning was developed. SIGPA generates a population whose individuals move in a greedy approach based on A∗ algorithm to search the solution space from different directions. It can be used to find an optimal path for every graph-based problem under various objectives. To test SIGPA, a generic MOTRP formulation is proposed. A generic TRP formulation remains a challenge since it has not been studied thoroughly in the literature. To this end, a novel mixed binary quadratic programming model is proposed for generating personalized TRP based on multi-objective criteria and user preferences, supporting, also, electric vehicles or sensitive social groups in outdoor cultural environments. The model targets to optimize the route under various factors that the user can choose, such as travelled distance, smoothness of route without multiple deviations, safety and cultural interest. The proposed model was compared to five SOA models for addressing TRP problems in 120 various scenarios solved with CPLEX solver and SIGPA. SIGPA was also tested in real scenarios with A* algorithm. The results proved the effectiveness of our model in terms of optimality but also the efficiency of SIGPA in terms of computing time. The convergence and the fitness landscape analysis showed that SIGPA achieved quality solutions with stable convergence.

ACS Style

Charis Ntakolia; Dimitris K. Iakovidis. A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning. Computers & Operations Research 2021, 133, 105358 .

AMA Style

Charis Ntakolia, Dimitris K. Iakovidis. A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning. Computers & Operations Research. 2021; 133 ():105358.

Chicago/Turabian Style

Charis Ntakolia; Dimitris K. Iakovidis. 2021. "A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning." Computers & Operations Research 133, no. : 105358.

Journal article
Published: 11 February 2021 in Diagnostics
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Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

ACS Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitrios Tsaopoulos. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics 2021, 11, 285 .

AMA Style

Charis Ntakolia, Christos Kokkotis, Serafeim Moustakidis, Dimitrios Tsaopoulos. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics. 2021; 11 (2):285.

Chicago/Turabian Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitrios Tsaopoulos. 2021. "Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients." Diagnostics 11, no. 2: 285.

Research article
Published: 11 January 2021 in SN Applied Sciences
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Route planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.

ACS Style

Charis Ntakolia; Dimitris K. Iakovidis. A route planning framework for smart wearable assistive navigation systems. SN Applied Sciences 2021, 3, 1 -18.

AMA Style

Charis Ntakolia, Dimitris K. Iakovidis. A route planning framework for smart wearable assistive navigation systems. SN Applied Sciences. 2021; 3 (1):1-18.

Chicago/Turabian Style

Charis Ntakolia; Dimitris K. Iakovidis. 2021. "A route planning framework for smart wearable assistive navigation systems." SN Applied Sciences 3, no. 1: 1-18.

Review
Published: 05 January 2021 in Energy Systems
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Driven by the continuous growing demand for heating and cooling, district heating and cooling systems (DHC) play a major role in the field of energy by providing environmentally friendly solutions for citizens with significant economic impact. Taken also into account the global need for greener and smarter cities, optimization and automation of current DHC operation is more imminent than ever. In order to achieve a transformation of DHC systems, new data-driven technologies are being adopted to reach the goals. In this paper the findings of a systematic literature review are presented covering articles published in the last decades in which the authors described the development and application of machine learning approaches to the DHC sector. In total, 74 articles were retrieved, analysed and categorized into two main categories: (i) heating load/demand prediction and (ii) design, maintenance and scheduling. The survey findings are presented and listed in terms of the machine learning techniques mentioned therein (supervised learning, unsupervised learning and reinforcement learning), the specific application domain (load forecast, design, maintenance and scheduling) of each article providing also insights regarding the source data used and the quality of the results.

ACS Style

Charis Ntakolia; Athanasios Anagnostis; Serafeim Moustakidis; Nikolaos Karcanias. Machine learning applied on the district heating and cooling sector: a review. Energy Systems 2021, 1 -30.

AMA Style

Charis Ntakolia, Athanasios Anagnostis, Serafeim Moustakidis, Nikolaos Karcanias. Machine learning applied on the district heating and cooling sector: a review. Energy Systems. 2021; ():1-30.

Chicago/Turabian Style

Charis Ntakolia; Athanasios Anagnostis; Serafeim Moustakidis; Nikolaos Karcanias. 2021. "Machine learning applied on the district heating and cooling sector: a review." Energy Systems , no. : 1-30.

Journal article
Published: 18 November 2020 in Healthcare
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Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagnosis of bone metastasis a challenge. Artificial intelligence can play a crucial role as a decision support tool to alleviate the burden of generating manual annotations on images and therefore prevent oversights by medical experts. So far, several state-of-the-art convolutional neural networks (CNN) have been employed to address bone metastasis diagnosis as a binary or multiclass classification problem achieving adequate accuracy (higher than 90%). However, due to their increased complexity (number of layers and free parameters), these networks are severely dependent on the number of available training images that are typically limited within the medical domain. Our study was dedicated to the use of a new deep learning architecture that overcomes the computational burden by using a convolutional neural network with a significantly lower number of floating-point operations (FLOPs) and free parameters. The proposed lightweight look-behind fully convolutional neural network was implemented and compared with several well-known powerful CNNs, such as ResNet50, VGG16, Inception V3, Xception, and MobileNet on an imaging dataset of moderate size (778 images from male subjects with prostate cancer). The results prove the superiority of the proposed methodology over the current state-of-the-art on identifying bone metastasis. The proposed methodology demonstrates a unique potential to revolutionize image-based diagnostics enabling new possibilities for enhanced cancer metastasis monitoring and treatment.

ACS Style

Charis Ntakolia; Dimitrios Diamantis; Nikolaos Papandrianos; Serafeim Moustakidis; Elpiniki Papageorgiou. A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients. Healthcare 2020, 8, 493 .

AMA Style

Charis Ntakolia, Dimitrios Diamantis, Nikolaos Papandrianos, Serafeim Moustakidis, Elpiniki Papageorgiou. A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients. Healthcare. 2020; 8 (4):493.

Chicago/Turabian Style

Charis Ntakolia; Dimitrios Diamantis; Nikolaos Papandrianos; Serafeim Moustakidis; Elpiniki Papageorgiou. 2020. "A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients." Healthcare 8, no. 4: 493.

Long paper
Published: 20 October 2020 in Universal Access in the Information Society
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The marginalization of people with disabilities, such as visually impaired individuals (VIIs), has driven scientists to take advantage of the fast growth of smart technologies and develop smart assistive systems (SASs) to bring VIIs back to social life, education and even to culture. Our research focuses on developing a human–computer interactive system that will guide VIIs in outdoor cultural environments by offering universal access to cultural information, social networking and safe navigation among other services. The VI users interact with computer-based SAS to control the system during its operation, while having access to remote connection with non-VIIs for external guidance and company. The development of such a system needs a user-centered design (UCD) that incorporates the elicitation of the necessary requirements for a satisfying operation for the VI users. In this paper, we present a novel SAS system for VIIs and its design considerations, which follow a UCD approach to determine a set of operational, functional, ergonomic, environmental and optional requirements of the system. Both VIIs and non-VIIs took part in a series of interviews and questionnaires, from which data were analyzed to form the requirements of the system for both the on-site and remote use. The final requirements are tested by trials and their evaluation and results are presented. The experimental investigations gave significant feedback for the development of the system, throughout the design process. The most important contribution of this study is the derivation of requirements applicable not only to the specific system under investigation, but also to other relevant SASs for VIIs.

ACS Style

Charis Ntakolia; George Dimas; Dimitris K. Iakovidis. User-centered system design for assisted navigation of visually impaired individuals in outdoor cultural environments. Universal Access in the Information Society 2020, 1 -26.

AMA Style

Charis Ntakolia, George Dimas, Dimitris K. Iakovidis. User-centered system design for assisted navigation of visually impaired individuals in outdoor cultural environments. Universal Access in the Information Society. 2020; ():1-26.

Chicago/Turabian Style

Charis Ntakolia; George Dimas; Dimitris K. Iakovidis. 2020. "User-centered system design for assisted navigation of visually impaired individuals in outdoor cultural environments." Universal Access in the Information Society , no. : 1-26.

Conference paper
Published: 01 October 2020 in 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)
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Osteoarthritis is the common form of arthritis in the knee (KOA). It is identified as one of the main causes of pain leading even to disability. To exploit the continuous increase in medical data concerning KOA, various studies employ big data and Artificial Intelligence analytics for KOA prognosis or treatment. However, most of the studies are limited to either specific groups of patients or specific groups of features, such as MRI, X-ray images or questionnaires. In this study, a machine learning pipeline is proposed to predict knee joint space narrowing (JSN) in KOA patients. The proposed methodology, that is based on multidisciplinary data from the osteoarthritis initiative (OAI) database, employs: (i) a clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection process consisting of filter, wrapper and embedded techniques that identifies the most informative risk factors that contribute to JSN prediction; and (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final prediction model for JSN. The evaluation was conducted with respect to model’s overall performance, robustness and highest achieved accuracy. A 78.3% and 77.7% accuracy were achieved in left and right leg by Logistic Regression on the group of the 164 risk factors and SVM on the group of the 88 and 90 risk factors, respectively.

ACS Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitris Tsaopoulos. A machine learning pipeline for predicting joint space narrowing in knee osteoarthritis patients. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) 2020, 934 -941.

AMA Style

Charis Ntakolia, Christos Kokkotis, Serafeim Moustakidis, Dimitris Tsaopoulos. A machine learning pipeline for predicting joint space narrowing in knee osteoarthritis patients. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). 2020; ():934-941.

Chicago/Turabian Style

Charis Ntakolia; Christos Kokkotis; Serafeim Moustakidis; Dimitris Tsaopoulos. 2020. "A machine learning pipeline for predicting joint space narrowing in knee osteoarthritis patients." 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) , no. : 934-941.

Original paper
Published: 22 August 2019 in Optimization Letters
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The growth in demand for air transport has generated new challenges for capacity and safety. In response, manufacturers develop new types of aircraft while airlines open new routes and adapt their fleet. This excessive demand for air transport also leads to the need for further investments in airport expansion and ATM modernization. The current work was focused on the ATM problem with respect to new procedures, such as free flight, for addressing the air capacity issues in an environmental approach. The study was triggered by and aligned with the following performance objectives set by EUROCONTROL and the European Commission: (1) to improve ATM safety whilst accommodating air traffic growth; (2) to increase the ATM network efficiency; (3) to strengthen ATM’s contribution to aviation security and to environmental objectives; (4) to match capacity and air transport growth. The proposed mathematical model covers the aforementioned objectives by focusing on energy losses and costs of flights under the scenario of a controlled free flight and a unified airspace. The factors enhanced in the model were chosen based on their impact on the ATM energy efficiency, such as the airborne delays and flight duration, the delays due to ground holding, the flight cancellation, the flight speed deviations and the flight level alterations. Therefore, the presented mathematical model minimizes the energy costs due to the above terms under certain assumptions and constraints. Finally, simulation case studies, used as proof tests, have been conducted under different ATM scenarios to examine the complexity and the efficiency of the developed model.

ACS Style

Charis Ntakolia; Hernan Caceres; John Coletsos. A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects. Optimization Letters 2019, 14, 1 -22.

AMA Style

Charis Ntakolia, Hernan Caceres, John Coletsos. A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects. Optimization Letters. 2019; 14 (7):1-22.

Chicago/Turabian Style

Charis Ntakolia; Hernan Caceres; John Coletsos. 2019. "A dynamic integer programming approach for free flight air traffic management (ATM) scenario with 4D-trajectories and energy efficiency aspects." Optimization Letters 14, no. 7: 1-22.

Chapter
Published: 02 July 2019 in Advanced Controllers for Smart Cities
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Visual impairment restricts everyday mobility and limits the accessibility of places, which for the non-visually impaired is taken for granted. A short walk to a close destination, such as a market or a school becomes an everyday challenge. In this chapter, we present a novel solution to this problem that can evolve into an everyday visual aid for people with limited sight or total blindness. The proposed solution is a digital system, wearable like smart-glasses, equipped with cameras. An intelligent system module, incorporating efficient deep learning and uncertainty-aware decision-making algorithms, interprets the video scenes, translates them into speech, and describes them to the user through audio. The user can almost naturally interact with the system via a speech-based user interface, which is also capable of understanding the user’s emotions. The capabilities of this system are investigated in the context of accessibility and guidance to outdoor environments of cultural interest, such as the historic triangle of Athens. A survey of relevant state-of-the-art systems, technologies and services is performed, identifying critical system components that better adapt to the goals of the system, user needs and requirements, toward a user-centered architecture design.

ACS Style

Dimitris K. Iakovidis; Dimitrios Diamantis; George Dimas; Charis Ntakolia; Evaggelos Spyrou. Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired. Advanced Controllers for Smart Cities 2019, 237 -271.

AMA Style

Dimitris K. Iakovidis, Dimitrios Diamantis, George Dimas, Charis Ntakolia, Evaggelos Spyrou. Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired. Advanced Controllers for Smart Cities. 2019; ():237-271.

Chicago/Turabian Style

Dimitris K. Iakovidis; Dimitrios Diamantis; George Dimas; Charis Ntakolia; Evaggelos Spyrou. 2019. "Digital Enhancement of Cultural Experience and Accessibility for the Visually Impaired." Advanced Controllers for Smart Cities , no. : 237-271.

Conference paper
Published: 15 May 2019 in Communications in Computer and Information Science
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Obstacle detection addresses the detection of an object, of any kind, that interferes with the canonical trajectory of a subject, such as a human or an autonomous robotic agent. Prompt obstacle detection can become critical for the safety of visually impaired individuals (VII). In this context, we propose a novel methodology for obstacle detection, which is based on a Generative Adversarial Network (GAN) model, trained with human eye fixations to predict saliency, and the depth information provided by an RGB-D sensor. A method based on fuzzy sets are used to translate the 3D spatial information into linguistic values easily comprehensible by VII. Fuzzy operators are applied to fuse the spatial information with the saliency information for the purpose of detecting and determining if an object may interfere with the safe navigation of the VII. For the evaluation of our method we captured outdoor video sequences of 10,170 frames in total, with obstacles including rocks, trees and pedestrians. The results showed that the use of fuzzy representations results in enhanced obstacle detection accuracy, reaching 88.1%.

ACS Style

George Dimas; Charis Ntakolia; Dimitris K. Iakovidis. Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation. Communications in Computer and Information Science 2019, 533 -544.

AMA Style

George Dimas, Charis Ntakolia, Dimitris K. Iakovidis. Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation. Communications in Computer and Information Science. 2019; ():533-544.

Chicago/Turabian Style

George Dimas; Charis Ntakolia; Dimitris K. Iakovidis. 2019. "Obstacle Detection Based on Generative Adversarial Networks and Fuzzy Sets for Computer-Assisted Navigation." Communications in Computer and Information Science , no. : 533-544.

Original paper
Published: 01 October 2015 in Energy Systems
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The insufficient air routes combined with the adverse weather and congestion to air sectors lead to economic, environmental and safety problems to political aviation in Europe. This situation creates negative aspects to airlines and airports, as well. Furthermore, according to recent studies over 40,000 daily flights are predicted for 2020, and therefore the current ATM system will not be able to handle this volume of traffic in an efficient manner. A new promising approach of solving these problems in the future consists of transforming the ATM system from an ‘airport-centered’ to an ‘airplane-centered’ system so it can: (i) increase safety and energy efficiency, (ii) support the free flight concept, (iii) distribute fairly ground-holding and air delays among the flights, (iv) minimize the volume of work of ATCs as an observer, (v) relax the existing distance limits between airplane since the human factor has been annihilated, and therefore, (vi) increase the air sectors’ capacity avoiding congestions and (vii) prioritize the airline preferences. Our attempt will be to develop a mathematical model for a support system for the free flight concept. We divide the problem into two sub-problems (upper and lower level) in order to decrease the computational efforts and the complexity of the air traffic flow management problem and to allow flexibility, supporting in the same time the free flight scenario.

ACS Style

John Coletsos; Charis Ntakolia. Air traffic management and energy efficiency: the free flight concept. Energy Systems 2015, 8, 709 -726.

AMA Style

John Coletsos, Charis Ntakolia. Air traffic management and energy efficiency: the free flight concept. Energy Systems. 2015; 8 (4):709-726.

Chicago/Turabian Style

John Coletsos; Charis Ntakolia. 2015. "Air traffic management and energy efficiency: the free flight concept." Energy Systems 8, no. 4: 709-726.