COVID-19 Predictive Models Based on Grammatical Evolution.

A feature construction method that incorporates a grammatical guided procedure is presented here to predict the monthly mortality rate of the COVID-19 pandemic. Three distinct use cases were obtained from publicly available data and three corresponding datasets were created for that purpose. The proposed method is based on constructing artificial features from the original ones.

A comparative study on recognizing human activities by applying diverse Machine Learning approaches.

This paper deals with the problem of identifying and recognizing everyday human activities. The main goal is to compare a variety of implemented classification models founded on diverse machine learning approaches; one that utilizes features extracted from the time and frequency domain and three others that take advantage of the attributes of the symbolic space in order to extract conclusions regarding the performance and the potential usefulness of each of them. To guarantee the impartiality of the comparison, we used the signals contained in a free accessible dataset, which are subjected to the same preprocessing, and divided into equal time-length windows.

Industrial Needs in the Fields of Artificial Intelligence, Internet of Things and Edge Computing.

Industry 4.0 corresponds to the Fourth Industrial Revolution, resulting from technological innovation and research multidisciplinary advances. Researchers aim to contribute to the digital transformation of the manufacturing ecosystem both in theory and mainly in practice by identifying the real problems that the industry faces.

Role of Academics in Transferring Knowledge and Skills on Artificial Intelligence, Internet of Things and Edge Computing.

Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC).

Transformation of Robotics Education in the Era of Covid-19: Challenges and Opportunities.

The COVID-19 pandemic has significantly impacted many aspects of our social and professional life. To this end, Higher Education institutions reacted rather vastly to this unpreceded situation although many issues have been reported in the international literature since the emergence of the first global lockdown. As we are now transitioning back to the 'normality', universities and businesses consider the so-called 'blended' or 'hybrid' model as a means of facilitating the transition phase.

Track My Health: An IoT Approach for Data Acquisition and Activity Recognition.

Human Activity Recognition (HAR) is an arisen research topic because of its usage of self-care and prevention issues. In our days, the advances of technology (smart-phones, smart-watches, tablets, wristbands) and achievements of Machine Learning provide great opportunities for in-depth research on HAR. Technological gadgets include many sensors that gather various, which in turn are input to machine learning techniques to derive useful information and results about human activities and health conditions.

A Machine Learning Approach for Human Activity Recognition.

Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life.

PortWeather: A Lightweight Onboard Solution for Real-Time Weather Prediction.

Maritime journeys significantly depend on weather conditions, and so meteorology has always had a key role in maritime businesses. Nowadays, the new era of innovative machine learning approaches along with the availability of a wide range of sensors and microcontrollers creates increasing perspectives for providing on-board reliable short-range forecasting of main meteorological variables. The main goal of this study is to propose a lightweight on-board solution for real-time weather prediction.

Investigating pH based evaluation of fetal heart rate (FHR) recordings.

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording.

An ordinal classification approach for CTG categorization.

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems.

A WebGIS platform to monitor environmental conditions in ports and their surroundings in South Eastern Europe.

The scope of this work is to describe the design and development of a web-based Geographic Information System (GIS) application and highlight its usefulness regarding monitoring and evaluating environmental conditions in several ports and their surroundings in the greater South East Europe (SEE). The system receives inputs and handles two kinds of data that are processed and illustrated through maps and graphs at various temporal and spatial scales in this informational platform. The aforementioned data consists of point measurements from stations operating in the area of SEE ports as well as satellite date sets derived monthly for a period of 10 to 12 years, in terms of sea surface temperature, chlorophyll a, and colored dissolved organic matter (CDOM).

Fuzzy Cognitive Map scenario-based medical decision support systems for education.

Soft Computing (SC) techniques are based on exploiting human knowledge and experience and they are extremely useful to model any complex decision making procedure. Thus, they have a key role in the development of Medical Decision Support Systems (MDSS). The soft computing methodology of Fuzzy Cognitive Maps has successfully been used to represent human reasoning and to infer conclusions and decisions in a human-like way and thus, FCM-MDSSs have been developed.

A three class treatment of the FHR classification problem using latent class analysis labeling.

Electronic Fetal Monitoring in the form of cardiotocography is routinely used for fetal assessment both during pregnancy and delivery. However its interpretation requires a high level of expertise and even then the assessment is somewhat subjective as it has been proven by the high inter and intra-observer variability. Therefore the scientific community seeks for more objective methods for its interpretation.

Wavelet analysis for detection of phasic electromyographic activity in sleep: influence of mother wavelet and dimensionality reduction.

Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches.

Fuzzy cognitive maps for medical decision support - a paradigm from obstetrics.

Medical Decision Support Systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy Cognitive Maps (FCMs) is a soft computing technique for modeling complex systems following an approach similar to human reasoning and decision-making. FCMs successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system.

Comparison of seven approaches for holter ECG clustering and classification.

In this work we present a comparative study, testing selected methods for clustering and classification of holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats Some of the tested methods represent the state of the art in pattern analysis, while others are novel algorithms developed by us. All the algorithms were tested on the same datasets, namely the MIT-BIH and the AHA databases.

Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework.

Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered.

A combined Fuzzy Cognitive Map and decision trees model for medical decision making.

Fuzzy Cognitive Maps (FCMs) are an efficient modeling method providing flexibility on the simulated system's design. They consist of nodes-concepts and weighted edges that connect the nodes and represent the cause and effect relationships among them. The performance of FCMs is dependent on the initial weight setting and architecture.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented.

Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines.

Cardiotocography is the main method used for fetal assessment in every day clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis.

A fuzzy cognitive map approach to differential diagnosis of specific language impairment.

This paper presents a computer-based model for differential diagnosis of specific language impairment (SLI), a language disorder that, in many cases, cannot be easily diagnosed. This difficulty necessitates the development of a methodology to assist the speech therapist in the diagnostic process. The methodology tool is based on fuzzy cognitive maps and constitutes a qualitative and quantitative computer model comprised of the experience and knowledge of specialists.

An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps.

The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure.