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). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers' perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities' curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.

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. Researchers focus on providing practical solutions using technologies such as the Industrial Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing (EC). On the other hand, universities educate young engineers and researchers by formulating a curriculum that prepares graduates for the industrial market. This research aimed to investigate and identify the industry's current problems and needs from an educational perspective. The research methodology is based on preparing a focused questionnaire resulting from an extensive recent literature review used to interview representatives from 70 enterprises operating in 25 countries. The produced empirical data revealed (1) the kind of data and business management systems that companies have implemented to advance the digitalization of their processes, (2) the industries' main problems and what technologies (could be) implemented to address them, and (3) what are the primary industrial needs and how they can be met to facilitate their digitization. The main conclusion is that there is a need to develop a taxonomy that shall include industrial problems and their technological solutions. Moreover, the educational needs of engineers and researchers with current knowledge and advanced skills were underlined.

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. The system is composed of a commercial weather station integrated with an industrial IOT-edge data processing module that computes the wind direction and speed forecasts without the need of an Internet connection. A regression machine learning algorithm was chosen so as to require the smallest amount of resources (memory, CPU) and be able to run in a microcontroller. The algorithm has been designed and coded following specific conditions and specifications. The system has been tested on real weather data gathered from static weather stations and onboard during a test trip. The efficiency of the system has been proven through various error metrics.

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). The WebGIS platform is based on the client-server model and uses Google Maps API services for data plotting. Advanced designing and development tools and methodologies are used. The available valuable data render the application into a trustful and accurate provider of visual environmental interest information regarding the main ports of southeastern Europe and their surroundings that would operate as a guide for an environmentally sustainable future of ports and sea corridors in SEE.

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. After the artificial features are generated, the original data set is modified based on these features and a machine learning model, such as an artificial neural network, is applied to the modified data. From the comparative experiments done, it was clear that feature construction has an advantage over other machine learning methods for predicting pandemic elements.

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. The Nearest Neighour classifier is applied to compare the four approaches.

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. In view of this decision, several studies can be identified wherein blended learning scenarios are proposed and described. The present work constitutes such an effort. Precisely, while adjusting the lens to the didactic of Robotics courses, we propose a blended learning model via which the laboratory activities are performed without the physical presence of the students in the physical context. The aforementioned objective is attained under the aid of the Virtual Reality technology coupled with the Digital Twin model. We hope that the ideas presented in this manuscript will motivate and inspire more researchers, instructional designers, and educators to consider the adoption of such alternative instructional techniques to mitigate the shortcomings that the remote education setting brings and further to improve the overall learning experience.

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. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).

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. Activity Recognition is mainly based physical sensors attached to the human body, with wearable devices coming with built-in sensors such as the accelerometer, gyroscope. This work presents a system based on the Internet of Things (IoT), that monitoring essential vital signals. A mobile application has designed and developed to collect data from a wearable device with built-in sensors (accelerometer and gyroscope) for different human activities and store them for use in a database. The purpose of this work is to present the module of the system that is responsible for the data acquisition, processing and storage of signals that will feed then the Machine Learning module to identify the human health status.

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. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

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. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms.

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. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting, therefore, a promising new automatic methodology for the prediction of metabolic acidosis.

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. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.

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. The development of the model was based on knowledge from the literature and then it was successfully tested on four clinical cases. The results obtained point to its final integration in the future and to its valid contribution as a differential diagnosis model of SLI.

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. Such systems are able to assist in critical decision-making, support diagnosis procedures and consult medical professionals. Here a new methodology is introduced to expand the utilization of FCM-MDSS for learning and educational purposes using a scenario-based learning (SBL) approach. This is particularly important in medical education since it allows future medical professionals to safely explore extensive "what-if" scenarios in case studies and prepare for dealing with critical adverse events.

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. Along this path, presented work proposes a classification approach, which is based on a latent class analysis method that attempts to produce more objective labeling of the training cases, a step which is vital in a classification problem. The method is combined with a simple logistic regression approach under two different schemes: a standard multi-class classification formulation and an ordinal classification one. The results are promising suggesting that more effort should be put in this proposed approach.

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. Feature extraction included 1s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets.

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. Medical Decision Systems are complex systems that can be decomposed to subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with varying degrees. Here a Medical Decision Support System based on an appropriate FCM architecture is proposed and developed, as well as a corresponding paradigm from obstetrics is described.

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. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy Cognitive Maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and/or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.

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. The results for all the employed methods are compared and evaluated using the measures of sensitivity and specificity.