Kostas Diamantaras

International Hellenic University · Information and Electronic Engineering

Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules:(i) a dyn...

This study aims to evaluate the impact of using augmented reality, gamification, and serious games in computer science education. The study presents the development process of an educational mobile application, describes an experiment that was conducted and involved 117 higher education students, and analyzes the results of a 49-item paper-based qu...

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issue...

Purpose In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for result...

Citation: Delianidi, M.; Diamantaras, K.; Tektonidis, D.; Salampasis, M. Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling. Appl. Sci. 2023, 13, 394. Abstract: Conventional recommendation methods such as collaborative filtering cannot be applied when long-term user models are not available. In this paper, we propose two se...

This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010...

This paper presents a system that automates activation of events that improve the accessibility and enhance the experience in theatrical performances in real time and proposes and evaluates the core method employed therein. This method aligns a given set of subtitles that is created and synchronized by experts for a given “rehearsal” audio stream,...

Alzheimer's disease (AD) is the main cause of dementia and Mild cognitive impairment (MCI) is a prodromal stage of AD whose early detection is considered crucial as it can contribute in slowing the progression of AD. In our study we attempted to classify a subject into AD, MCI, or Healthy Control (HC) groups with the use of electroencephalogram (EE...

This study scrutinizes the existing literature regarding the use of augmented reality and gamification in education to establish its theoretical basis. A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was conducted. To provide complete and valid information, all types...

Despite the fact that in some areas of cultural life, as in the case of certain online video platforms or TV programs, notable progress has been made to provide content accessible to Deaf and Hard of Hearing people (DHH), the same cannot be said for live theater performances. In this work, a system called NLP-Theatre is presented , with the emphasi...

p>The work in this paper is an extended research of our previous work: M. Delianidi, K. Diamantaras, G. Chrysogonidis, and V. Nikiforidis,“Student performance prediction using dynamic neural models,” in Fourteenth International Conference on Educational Data Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of predicting a student...

p>The work in this paper is an extended research of our previous work: M. Delianidi, K. Diamantaras, G. Chrysogonidis, and V. Nikiforidis,“Student performance prediction using dynamic neural models,” in Fourteenth International Conference on Educational Data Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of predicting a student...

p>The work in this paper is an extended research of our previous work: M. Delianidi, K. Diamantaras, G. Chrysogonidis, and V. Nikiforidis,“Student performance prediction using dynamic neural models,” in Fourteenth International Conference on Educational Data Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of predicting a student...

In the present study, machine learning models and, more specifically, Artificial Neural Networks and Recurrent Neural Networks are applied in order to simulate the relationship between recharge wells rates and chloride concentrations in the aquifer of Nea Moudania, Chalkidiki. To obtain the necessary data related to the output of the models, a tran...

With a view to creating a mixed reality that combines coexisting real and virtual objects and to providing users with real-time access to information in an interactive manner, augmented reality enriches users’ physical environment by incorporating digital and real objects and rendering them in the physical environment in the proper time and spatial...

Many thousands of patent applications arrive at patent offices around the world every day. One important subtask when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical patent classification schemes that will enable routing of the patent application to a patent examiner who is knowledge...

We consider joint beamforming and relay motion control in mobile relay beamforming networks, operating in a spatio-temporally varying channel environment. A time slotted approach is adopted, where in each slot, the relays implement optimal beamforming, and estimate their optimal positions for the next slot. % We place the problem of relay motion co...

Cognitive disorders, including Alzheimer’s Disease (AD), are health issues concerning all society. The evolution of technology and Artificial Intelligence (AI)/ Machine Learning (ML) in the health domain promises an earlier and more accurate diagnosis for Alzheimer’s disease and dementia. In this study, we examine Healthy patients and patients with...

In the era of the 4th industrial revolution, a key challenge for the industries is the efficient reduction of the production cost caused by malfunctioning equipment. This paper proposes a Fault Detection and Diagnosis (FDD) framework for Non-Linear Processes utilizing Dynamic Neural Networks and feature reduction methods. We investigate both types...

We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate with...

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a learning approach to synthesize t...

We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions in the course of their learning and evaluation process. We model the student performance as a dynamic problem and compare the two major classes of dynamic neural architectures for its solution, namely the fin...

MIMO transmit arrays allow for flexible design of the transmit beampattern. However, the large number of elements required to achieve certain performance using uniform linear arrays (ULA) maybe be too costly. This motivated the need for thinned arrays by appropriately selecting a small number of elements so that the full array beampattern is preser...

Multidimensional factor and cluster analysis and embedding-based machine learning were evaluated toward a knowledge-based recommendation system for supermarket e-marketing. The goal was to produce personalized notifications on special offers, optimized per individual customer’s predicted response. To this purpose, we firstly applied Multiple Corres...

In this paper, we propose an energy-efficient radar beampattern design framework for a Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a machine learning approach to syn...

We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an e-commerce web site, thus seemingly prepare the necessary data infrastructure for the recommendation algorithm to operate wit...

INTRODUCTION: Sleep stage classification is an important task for the timely diagnosis of sleep-related disorders, which are one the most common indicator of illness. OBJECTIVE: An automated sleep scoring implementation with promising generalization capabilities is presented, aiding towards eliminating the tedious procedure of manual sleep scoring....

The effectiveness of the k-NN classifier is highly dependent on the value of the parameter k that is chosen in advance and is fixed during classification. Different values are appropriate for different datasets and parameter tuning is usually inevitable. A dataset may include simultaneously well-separated and not well-separated classes as well as n...

The growth rates of today’s societies and the rapid advances in technology have led to the need for access to dynamic, adaptive and personalized information in real time. Augmented reality provides prompt access to rapidly flowing information which becomes meaningful and “alive” as it is embedded in the appropriate spatial and time framework. Augme...

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. The architecture model that was used is introduced. The greek version of the spaCy platform was added into...

Data reduction aims to reduce the number of training data in order to speed-up the classifier training. They do that by collecting a small set of representative prototypes from the original patterns. The Reduction by finding Homogeneous Clusters algorithm is a simple data reduction technique that recursively utilizes k-means clustering to build a s...

We consider the communication of a source-destination pair in a dynamic flat fading channel environment. The communication is aided by mobile relays, which beamform the source signal to the destination. The beamforming weights and the relay positions are optimally selected so that the average Signal-to-Interference and Noise ratio (SINR) at the des...

This paper proposes a machine learning approach to part-of-speech tagging and named entity recognition for Greek, focusing on the extraction of morphological features and classification of tokens into a small set of classes for named entities. Standard method-ologies for these tasks are presented and the architecture model that was used is introduc...

Data reduction, achieved by collecting a small subset of representative prototypes from the original patterns, aims at alleviating the computational burden of training a classifier without sacrificing performance. We propose an extension of the Reduction by finding Homogeneous Clusters algorithm, which utilizes the k-means method to propose a set o...

Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleep-related disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring...

In this paper, preliminary results are presented on the development of an intelligent recommendation system, which enables personalised promotion campaigns through mobile channels. The current work is focused on the field of supermarket special offer promotions and is part of an ongoing research project on artificial intelligence technologies.

Industrial control systems (ICSs) manage and monitor critical civil or military infrastructure, such as water treatment facilities, power plants, electricity grids, transportation systems, oil and gas refineries, and health care. Because they are so important, ICSs are becoming attractive targets for malicious attacks that could lead to catastrophi...

Fake news has become a problem of great impact in our information driven society because of the continuous and intense fakesters content distribution. Information quality in news feeds is under questionable veracity calling for automated tools to detect fake news articles. Due to many faces of fakesters, creating such tool is a challenging problem....

Embedded devices, such as programmable logic controllers (PLCs) and Internet of Things (IoT) devices are becoming targets of malware attacks with increasing frequency and catastrophic results. Physical side-channel analysis is one way to monitor a device without accessing its software and, thus, without imposing on its resources. In this article, w...

One of the fundamental issues of hydrology is the rainfall-runoff relationship and streamflow forecasting that plays an important role in water balance. Up-to-date a large number of models have been developed to simulate the relationship of rainfall-runoff and forecasting the streamflow. In the present study a comparison between stochastic and mach...

Special equations called Pedotransfer Functions (PTFs) have been broadly implemented as state-of-the-art indirect cost-effective and time-saving methods in predicting soil bulk density (BD). The objectives of the present study are: a) to perform a comprehensive literature review and record published PTFs that estimate BD, b) to evaluate the perform...

Neural Networks and Support Vector Machines (SVMs) are two of the most popular and efficient supervised classification models. However, in the context of large datasets many complexity issues arise due to high memory requirements and high computational cost. In the context of the application of Data Mining algorithms, data reduction techniques atte...

Neural Networks and Support Vector Machines (SVMs) are two of the most popular and efficient supervised classification models. However, in the context of large datasets many complexity issues arise due to high memory requirements and high computational cost. In the context of the application of Data Mining algorithms, data reduction techniques atte...

This paper deals with the problem of airfare prices prediction. For this purpose a set of features characterizing a typical flight is decided, supposing that these features affect the price of an air ticket. The features are applied to eight state of the art machine learning (ML) models, used to predict the air tickets prices, and the performance o...

This paper proposes a neural network architecture for solving systems of non-linear equations. A back propagation algorithm is applied to solve the problem, using an adaptive learning rate procedure, based on the minimization of the mean squared error function defined by the system, as well as the network activation function, which can be linear or...

Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that aut...

In kernel based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classific...

This paper introduces a parallel implementation of the kernelized Slackmin algorithm able to tackle medium scale data in pattern classification applications. Initially, the main principles of the serial Slackmin algorithm are described, with emphasis to its parallel nature making its parallelization a straightforward task. The parallelization is ac...

Various internet services, including cloud providers and social networks collect large amounts of information that needs to be processed for statistical or other reasons without breaching user privacy. We present a novel approach where privacy protection can be viewed as a data transformation problem. The problem is formulated as a pair of classifi...

The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Ap...

Although Support Vector Machines (SVMs) are considered effective supervised learning methods, their training procedure is time-consuming and has high memory requirements. Therefore, SVMs are inappropriate for large datasets. Many Data Reduction Techniques have been proposed in the context of dealing with the drawbacks of $k$-Nearest Neighbor classi...

The objective of this research is the presentation of a neural network capable of solving complete nonlinear algebraic systems of n equations with n unknowns. The proposed neural solver uses the classical back propagation algorithm with the identity function as the output function, and supports the feature of the adaptive learning rate for the neur...

Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, social networks, message boards,...

Optical-wireless access networks constitute a quite attractive solution to meet the ever-increasing bandwidth requirements of end-users, offering significant benefits such as ubiquitous coverage in the wireless domain and huge bandwidth in the optical domain. However, converging optical and wireless networking technologies, with Passive Optical Net...

This paper presents an MLP-type neural network with some fixed connections and a backpropagation-type training algorithm that identifies the full set of solutions of a complete system of nonlinear algebraic equations with n equations and n unknowns. The proposed structure is based on a backpropagation-type algorithm with bias units in output neuron...

We present a comparative study on the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications industry. In the first phase of our experiments, all models were applied and evaluated using cross-validation on a popular, public domain dataset. In the second phase, the performan...

The objective of this research is the presentation of a feed-forward neural network capable of estimating the 2-cycle fixed points of Henon map by solving their defining nonlinear algebraic system. The network uses the back propagation algorithm and solves the aforementioned system for a set of values of the parameters α and β of Henon map. Besides...

Slack variables are utilized in optimisation problems in order to build soft margin classifiers that allow for more flexibility during training. A robust binary classification algorithm that is based on the minimisation of the energy of slack variables, called the Mean Squared Slack (MSS), is proposed in this paper. Initially, the algorithm is anal...

An efficient parallel implementation of the recently proposed Slackmin classification algorithm that minimizes the mean squared slack variables energy is proposed in this paper. The efficacy of the resulted scheme is demonstrated both in terms of accuracy and computation speed. The parallelization of the Slackmin algorithm is achieved in the framew...

Signals in various applications are often generated by linear combinations of quantized components. The analysis of data into such components is treated here as a matrix analysis problem. We first show that the component alphabet can always be normalized to the levels 0, ..., M-1, without loss of generality. Then we study certain conditions under w...