This study presents the validation of the Greek translation of the Godspeed Questionnaire Series (GQS) through an interactive workshop. Data were collected from 94 participants who assessed seven different wearable robotic devices in person and online. The validation examined psychometric characteristics including internal consistency reliability and factor structure preservation. The Greek translation successfully adapted semantic differentials while achieving equivalent performance compared to the original English version. Cross-language equivalence was maintained across most dimensions. All subscales demonstrated significant discriminative ability between robotic devices, validating the Greek GQS as a reliable instrument for human-robot interaction research and user assessment in wearable robotics applications.

ABSTRACT Although JavaScript dominates modern software development, research on its quality attributes remains scarce, despite the fundamental differences that distinguish it from other languages. This motivates dedicated research related to JavaScript quality attributes and metrics. This paper aims to identify (a) the quality attributes of the JavaScript language that are mainly studied and (b) the quality metrics that are used to quantify them. Additionally, the paper provides information on the tools that can be used to measure quality metrics. To achieve these goals, we have conducted a mapping study on seven journals and eight conferences of high quality. A total of 142 primary studies, published between 2002 and February 2025, have been selected and analyzed, to identify and classify software metrics to high‐level quality attributes, as described in ISO/IEC 25010:2011. Maintainability, Security, Reliability, and Usability quality attributes are the most studied ones. Furthermore, 78 generic and 48 JavaScript‐specific metrics were identified. A wide dispersion of metrics has been identified for assessing each quality attribute, based on different development tasks. Moreover, a variety of tools and benchmarks were identified. A clear research trend in JavaScript quality assessment related to issues that involve software reuse, code testing, and dynamic code analysis has been identified. Yet differences among primary studies in quality assessment and quantification, along with tool adoption indicate the need for further exploration of these recurring topics.

In this study, we present a novel approach to address the task of identifying nuclei in a diverse set of microscopy scan images containing slides from several distinct cancerous tissue types. Our approach is based on YOLOv5 algorithm. We thoroughly discuss the crucial training specifics that contributed to our outcomes. Additionally, we propose an intuitive strategy for combining multiple segmentation results to further improve the accuracy of nucleus identification. By employing this lightweight model, we successfully achieved results better or close to the state-of-the-art performance.

The Deep-in-Biopsies (DiB) platform supports diagnosing and staging diseases using patient biopsy imaging. This flexible service is designed for medical units to handle multiple organs and diseases. Specialists can train the system on demand using their selected images and annotations, which can be generated using an embedded annotation tool. The system proposes pathology findings to the expert for validation, and validated findings can be used for further training.

Alzheimer's disease and Frontotemporal dementia are the two most reported dementia cases. They both are neurodegenerative disorders without cure while existing treatments only halt their progress. Thus, early detection is of crucial importance. In this work, we utilize electroencephalographic signals of AD and FTD patients and propose a classification pipeline to distinguish them from healthy signals. This pipeline consists of Independent Component Analysis as a preprocessing stage, the extraction of time, frequency and complexity features, feature elimination through importance ranking and finally classification through utilizing Gradient Boosting Decision Trees. The proposed methodology achieved 92.27% F1 score in the Dementia versus Control problem, 83.06% in the AD versus Control and 80.67% in the FTD versus Control.

In the real world, consensus achievement is an adaptive and evolutionary process. Tailor-made mechanisms are engaging in the light of disputes to resolve emerging contradictions between the atoms. Most of the dominant blockchain architectures tend to oversee this fact. They come integrated with rigid, overkilling consensus-achievement procedures, which, in many cases, are not necessary or significantly beneficial. They often raise overwhelming demands on the nodes and prohibit the deployment of fully distributed and peer blockchain operation in the IoT world. Still, for the IoT ecosystems and the metaverse to become self-sustainable and self-evolving, robust validity mechanisms must be embedded in the atomic scale. This work investigates the process of building and sustaining scalable consensus policies over the trivial atomic capacities of the IoT ecosystems. For this, we deploy the IoT microblockchain framework as an atomic consistency tier and we define a primary set of validity rules that every node can easily carry out. The initial concept relies on Gödel’s incompleteness theorems and K. Friston’s free energy principle and is investigated under the perspectives of graph and game theory. To study the dynamic behavior of the system, a competitive game among the nodes is run. The Proof of Existence (PoE) is utilized as a universal proofing case. Its feasibility is demonstrated, and its complexity is analyzed under various system considerations. The findings prove that finite-capacity atoms can collectively and efficiently support verifiable validity and scalable consensus in the evolutionary IoT ecosystems and the metaverse, even under conditions of high diversity, trivial capacity, and eventual consistency.

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

In this manuscript, a methodology for analysing motor signals from Parkinson's disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson's Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.

The purpose of fetal monitoring during childbirth is the early recognition of any pathological conditions to guide a clinician in early intervention to avoid any complication in the health of the fetus. Non-Invasive Fetal Electrocardiography (NIFECG) represents an alternative fetal monitoring technique. The fetal ECG (fECG) derived from maternal thoracic and abdominal ECG recordings, provides an alternative to typical embryo monitoring means. In addition, it allows for long-term and ambulatory registrations that broaden the diagnostic capabilities for assessing the fetal health. However, in real situations, clear fECG is difficult to extract because it is usually overwhelmed by the dominant maternal ECG and other contaminated noise such as baseline wander and high-frequency interference. In this paper, a novel integrated adaptive methodology based on the combination of blind source separation, empirical mode decomposition, wavelet shrinkage denoising and correlation analysis, for the non-invasive extraction and processing of the FECG, is proposed. The methodology has been evaluated using both real and simulated recordings, and the obtained results indicate it efficiently.

This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) trains a series of SLNNs with different neuron types in the hidden layer utilizing the extreme learning machine (ELM) algorithm. These networks are evolved into heterogeneous networks (networks having different combinations of hidden neurons) with the help of a modified genetic algorithm (GA). The algorithm is able to handle two architecturally different neuron types: traditional low order (linear) units and higher order units with different transfer functions. The GA is fully self-adaptive and uses one novel hybrid crossover operator along with a self-adaptive mutation operator in order to retain ELM's simplicity and minimize the number of parameters need tuning. The experimental part of the current paper involves testing SA-He-HyELM with traditional ELM and other three ELM-based methods. The experimental part utilized a series of regression and classification experiments on relatively large datasets. In all cases the proposed method managed to get lower MSE or higher classification accuracy when compared to the aforementioned methods.

Objective characterization of pain intensity is necessary under certain clinical conditions. The portable electroencephalogram (EEG) is a cost-effective assessment tool and lately, new methods using efficient analysis of related dynamic changes in brain activity in the EEG recordings proved that these can reflect the dynamic changes of pain intensity. In this paper, a novel method for automated assessment of pain intensity using EEG data is presented. EEG recordings from twenty-two (22) healthy volunteers are recorded with the Emotiv EPOC+ using the Cold Pressor Test (CPT) protocol. The relative power of each brain band's energy for each channel is extracted and the stochastic forest algorithm is employed for discrimination across five classes, depicting the pain intensity. Obtained results in terms of classification accuracy reached high levels (72.7%), which renders the proposed method suitable for automated pain detection and quantification of its intensity.

In this paper, a novel stochastic approach for the induction of the decision trees in a tree-structured ensemble classifier is presented. The proposed algorithm is based on a stochastic process to induct each decision tree, assigning a probability for the selection of the split attribute in every tree node, designed in order to create strong and independent trees. A selection of 33 well-known classification datasets have been employed for the evaluation of the proposed algorithm, obtaining high classification results, in terms of Classification Accuracy, Average Sensitivity and Average Precision. Furthermore, a comparative study with Random Forest, Random Subspace and C4.5 is performed. The obtained results indicate the importance of the proposed algorithm, since it achieved the highest overall results in all metrics.

Sensor-based quantitative assessment of frailty in older adults usually requires expensive wearables or ambient sensors. However, low cost activity tracking systems can be effective in discriminating frailty characteristics. In this work, a system for assessment of the frailty of older people is presented, based on the analysis of data describing the daily in-house activity. More specifically, room-to-room movements are recorded using a set of Bluetooth beacons, located in fixed positions inside each room. A smartphone is used to locate the timestamps of room transitions, and these are used to calculate the time spent in each room, thus generating the time intervals signal. Then, several features depicting the in-house mobility are extracted from each signal and are evaluated, in terms of correlation with the subject's frailty status. Furthermore, the features are used to train a classifier for frailty assessment. Two approaches are evaluated, with different number of levels of frailty, and the results indicate that this procedure can lead to reliable older people frailty assessments, especially when two levels of frailty are considered.

Analysis of histopathological images covers a wide field of clinical practice in different pathological conditions. In many cases, biopsy images from different sources share similar characteristics. In this work, a method for image enhancement of biopsy images is proposed. During the first stage, the quality of the image is optimized, while in the second stage, a clustering technique is employed to separate the tissue from the background. For the evaluation of the method, Liver biopsies which have been extracted for the staging of Hepatitis C, are employed. The methodology has been tested using 19 liver biopsy images from patients who suffer from hepatic fibrosis and steatosis, obtaining detection Accuracy over 97% in pixel level.

In this work, an innovative classification algorithmic technique through sequential pattern mining was developed to predict the secondary structure of proteins. A basic algorithm was selected for the extraction of the sequential patterns and another algorithm was developed which employs these patterns for protein structure prediction. In the matter of predicting protein structures and scoring sequential patterns, several methodologies has been implemented that theoretically and experimentally overcome the disadvantages of existing algorithms.

The Decision Support tool of the myAirCoach platform is presented in this work. MyAirCoach is a web-based platform for personalized management of asthma patients. The Decision Support tool is used from medical experts to generate knowledge-based rules for asthma patients. The tool is a web-based application that includes a simple-to-use rule editor for generating medical rules in Arden Syntax, which is an HL7 standard. The logic of the rules in formulated in disjunctive normal form. Using the this tool, medical experts can generate rules and specify the set of rules that are applicable for alerting each patient, thus creating a personalized decision support system for asthma patients.

Non-Alcohol Liver Disease (NAFLD) is nowadays the most common liver disease in Western Countries. It is the chronic condition of fat expansion in liver, which is not associated with alcohol consumption. Quantitating steatosis in liver biopsies could provide objective measurement of the severity of the disease, instead of using semi-quantitative scoring systems. The current work, introduces an automated method for measuring steatosis in liver biopsies, using both machine learning and classical image processing techniques. Clustering is employed for tissue specimen detection, while an iterative morphological procedure is used for steatosis revealing. The method has been evaluated in a set of 20 liver biopsy images and the obtained results present ~1% mean percentage error.

Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized. In the first stage, a training set of images are employed to extract a hypercube for each class. Then, one centroid is initialized inside each hypercube and during the iterations of the clustering is allowed to move only inside the hypercube. For the evaluation of the proposed method 8 liver biopsy images are employed and classification results along with CPA values are computed for each image.

The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing. For the training of the classification scenario a five-classes dataset of EEG signals is employed in which two-classes have been recorded extracranially and the rest three intracranially including one class with epileptic seizure activity and two classes with seizure-free signals. Promising quantitative results are reported.

Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the worlds population with 1/3 of the epileptic patients not corresponding to anti-epileptic medication, epilepsy is constantly under the microscope and systems for automated detection of seizures are thoroughly examined. In this paper, a method for automated detection of epileptic activity is presented. The Discrete Wavelet Transform (DWT) is used to decompose the EEG recordings in several subbands and five features are extracted from the wavelet coefficients creating a set of features. The extracted feature vector is used to train a Support Vector Machine (SVM) classifier. Five classification problems are addressed, reaching high levels of overall accuracy ranging from 87% to 100%.

In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained.

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures, and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform and several t-f distributions are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: 1) t-f analysis and calculation of the PSD of each EEG segment; 2) feature extraction, measuring the signal segment fractional energy on specific t-f windows; and 3) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks. The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.

In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy modelpsilas parameters. Based on this methodology, the generated fuzzy model includes a set of parameters, which are all the parameters included in the sigmoid functions. In addition, local, global and class weights are included, thus the fuzzy model is optimized with respect to both sigmoid function parameters and weights. The class weight introduction, which is a novel approach, grants to the fuzzy model the ability to identify the individual importance of each class and thus more accurately reflect the underlying properties of the classes under examination, in the domain of application. The above described methodology is applied to five known classification problems, obtained from the UCI machine learning repository, and the obtained classification accuracy is high.

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.

A electroencephalographic (EEG) signals, concerning epileptic seizures, is proposed. First, segments of the EEG signals are analyzed using a time-frequency distribution and then, several features are extracted for each segment, representing the energy distribution over the time-frequency plane. Those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments (existence of epileptic seizure or not). The evaluation results are very promising, indicating overall accuracy from 89.4% to 99%.

This paper introduces an automated methodology for the extraction of fetal heart rate from cutaneous potential abdominal electrocardiogram (abdECG) recordings. A three-stage methodology is proposed. Having the initial recording, which consists of a small number of abdECG leads in the first stage, the maternal R-peaks and fiducial points (QRS onset and offset) are detected using time-frequency (t-f) analysis and medical knowledge. Then, the maternal QRS complexes are eliminated. In the second stage, the positions of the candidate fetal R-peaks are located using complex wavelets and matching theory techniques. In the third stage, the fetal R-peaks, which overlap with the maternal QRS complexes (eliminated in the first stage) are found using two approaches: a heuristic algorithm technique and a histogram-based technique. The fetal R-peaks detected are used to calculate the fetal heart rate. The methodology is validated using a dataset of eight short and ten long-duration recordings, obtained between the 20th and the 41st week of gestation, and the obtained accuracy is 97.47%. The proposed methodology is advantageous, since it is based on the analysis of few abdominal leads in contrast to other proposed methods, which need a large number of leads.

A three-stage method for fetal heart rate extraction, from abdominal ECG recordings, is proposed. In the first stage the maternal R-peaks and fiducial points (QRS onset and offset) are detected, using time-frequency analysis, and the maternal QRS complexes are eliminated. The second stage locates the positions of the candidate fetal R-peaks, using complex wavelets and pattern matching theory techniques. In the third stage, the fetal R-peaks that overlap with the maternal QRS complexes are found. The method is validated using a dataset of 4 long duration recordings and the obtained results indicate high detection ability of the method (96% accuracy)

A rule-based decision support system is presented for the diagnosis of coronary artery disease. The generation of the decision support system is realized automatically using a three stage methodology: (a) induction of a decision tree from a training set and extraction of a set of rules; (b) transformation of the set of rules into a fuzzy model and (c) optimization of the parameters of the fuzzy model. The system is evaluated using 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Ten fold cross validation was employed and the average sensitivity and specificity obtained was 80% and 65% respectively. Our approach provides diagnosis based on easily acquired features and, since it is rule based, is able to provide interpretation for the decisions made