Explainable AI-based identification of contributing factors to the mood state change in children and adolescents with pre-existing psychiatric disorders in the context of COVID-19-related lockdowns in Greece

The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state

Correction: Ntakolia et al. An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece. Healthcare 2022, 10, 149

Argyris Stringaris was initially included as an author in the original publication [...

An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents

Air traffic management models: an energy efficient approach of free flight scenario with 4d-trajectories

Air traffic is the sum of flights taking place in the airspace and airport maneuvering area. Air traffic management is guided by flight safety. Air Traffic Management is a relatively new field of research and development. Its creation is inextricably linked to the development of civil aviation. The modernization of Air Traffic Management is based on the creation of new technologies and equipment, the redevelopment of European airspace with a view to a single European airspace and the use of free flight or even some aspects of it. Therefore, this dissertation describes the current state of Air Traffic Management in European airspace, the need for evolution and the mathematical research that has been done so far. We present briefly the mathematical models of air traffic management that can be found in the bibliography following from the extensive presentation of the contribution of this dissertation in Air Traffic Management mathematical models and methodologies. Initially, a two-level nonlinear model for air traffic management is presented to reduce air and ground delays, speed variations, flight cancellations and track selection. Due to the complexity of the nonlinear model, it was considered necessary to create a linear unified model, based on the non-linear, to reduce costs due to ground holding delays, flight duration, speed variations, flight level alternations and flight cancellations. This model was planned and tested in various air traffic management simulation scenarios in an artificial airspace.Εναέρια κυκλοφορία είναι το σύνολο των πτήσεων που διεξάγονται στον εναέριο χώρο και στην περιοχή ελιγμών των αεροδρομίων. Η διαχείριση της εναέριας κυκλοφορίας έχει σαν γνώμονα την ασφάλεια των πτήσεων. Η Διαχείριση Εναέριας Κυκλοφορίας αποτελεί έναν σχετικά καινούργιο τομέα έρευνας και ανάπτυξης. Η δημιουργία του είναι άρρηκτα συνδεδεμένη με την ανάπτυξη της πολιτικής αεροπορίας. Ο εκσυγχρονισμός της Διαχείρισης της Εναέριας Κυκλοφορίας βασίζεται στη δημιουργία νέων τεχνολογιών και εξοπλισμού, στην αναδιαμόρφωση του ευρωπαϊκού εναέριου χώρου με σκοπό τη δημιουργία εν τέλει ενός ενιαίου εναέριου χώρου και τη χρήση της ελεύθερης πτήσης ή έστω κάποιων πτυχών αυτής. Επομένως, στην παρούσα εργασία περιγράφεται η σημερινή κατάσταση της Διαχείρισης Εναέριας Κυκλοφορίας στον ευρωπαϊκό εναέριο χώρο, η ανάγκη για εξέλιξη και η έρευνα από μαθηματικής απόψεως που έχει γίνει μέχρι τώρα. Παρουσιάζονται συνοπτικά τα μαθηματικά μοντέλα διαχείρισης εναέριας κυκλοφορίας που συναντώνται στη βιβλιογραφία και στη συνέχεια παρουσιάζεται εκτενώς η συμβολή της παρούσας διατριβής στη Διαχείριση της Εναέριας Κυκλοφορίας. Αρχικά, παρουσιάζεται ένα μη γραμμικό μοντέλο δύο επιπέδων για τη διαχείριση της εναέριας κυκλοφορίας με στόχο τη μείωση των κοστών λόγω εναέριων και εδαφικών καθυστερήσεων, εναλλαγών ταχύτητας, ακύρωσης πτήσεων και επιλογή τροχιάς. Λόγω της πολυπλοκότητας του μη γραμμικού μοντέλου, θεωρήθηκε αναγκαία η δημιουργία ενός γραμμικού ενιαίου μοντέλου, βασιζόμενο στο μη γραμμικό, με στόχο τη μείωση των κοστών λόγω εδαφικών καθυστερήσεων, διάρκειας πτήσης, εναλλαγών ταχύτητας, εναλλαγών επιπέδων πτήσης και ακύρωσης πτήσεων. Το εν λόγω μοντέλο προγραμματίστηκε και εξετάστηκε σε διάφορα σενάρια προσομοίωσης διαχείρισης εναέριας κυκλοφορίας σε ένα τεχνητό εναέριο χώρο

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time

Επιχειρησιακή Έρευνα-Εισαγωγή στο μη γραμμικό προγραμματισμό (Τετραγωνικός, Κυρτός, Διαχωρίσιμος, Μη κυρτός, Γεωμετρικός και Κλασματικός προγραμματισμός)

85 σ.Παρουσίαση της θεωρίας και των τύπων του μη γραμμικού προγραμματισμού που συναντάμε στην επιχειρησιακή έρευνα καθώς και εφαρμογών των διαφόρων τύπων του μη γραμμικού προγραμματισμού.Presentation of the theory and different types of non linear programming that we find in operational research. Plus we present applications of these types of non linear programming.Χάρις Δ. Ντακόλι

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

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

An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management

Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding cost, ordering cost, and backorder cost. Backorder occurs when a product is temporarily unavailable or out of stock and the customer places an order for future production and shipment. Therefore, stock unavailability and prolonged delays in product delivery will lead to additional production costs and unsatisfied customers, respectively. Thus, it is of high importance to develop models that will effectively predict the backorder rate in an inventory system with the aim of improving the effectiveness of the supply chain and, consequentially, the performance of the company. However, traditional approaches in the literature are based on stochastic approximation, without incorporating information from historical data. To this end, machine learning models should be employed for extracting knowledge of large historical data to develop predictive models. Therefore, to cover this need, in this study, the backorder prediction problem was addressed. Specifically, various machine learning models were compared for solving the binary classification problem of backorder prediction, followed by model calibration and a post-hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to material backorder. The results showed that the RF, XGB, LGBM, and BB models reached an AUC score of 0.95, while the best-performing model was the LGBM model after calibration with the Isotonic Regression method. The explainability analysis showed that the inventory stock of a product, the volume of products that can be delivered, the imminent demand (sales), and the accurate prediction of the future demand can significantly contribute to the correct prediction of backorders

An Explainable Machine Learning Model for Material Backorder Prediction in Inventory Management

Global competition among businesses imposes a more effective and low-cost supply chain allowing firms to provide products at a desired quality, quantity, and time, with lower production costs. The latter include holding cost, ordering cost, and backorder cost. Backorder occurs when a product is temporarily unavailable or out of stock and the customer places an order for future production and shipment. Therefore, stock unavailability and prolonged delays in product delivery will lead to additional production costs and unsatisfied customers, respectively. Thus, it is of high importance to develop models that will effectively predict the backorder rate in an inventory system with the aim of improving the effectiveness of the supply chain and, consequentially, the performance of the company. However, traditional approaches in the literature are based on stochastic approximation, without incorporating information from historical data. To this end, machine learning models should be employed for extracting knowledge of large historical data to develop predictive models. Therefore, to cover this need, in this study, the backorder prediction problem was addressed. Specifically, various machine learning models were compared for solving the binary classification problem of backorder prediction, followed by model calibration and a post-hoc explainability based on the SHAP model to identify and interpret the most important features that contribute to material backorder. The results showed that the RF, XGB, LGBM, and BB models reached an AUC score of 0.95, while the best-performing model was the LGBM model after calibration with the Isotonic Regression method. The explainability analysis showed that the inventory stock of a product, the volume of products that can be delivered, the imminent demand (sales), and the accurate prediction of the future demand can significantly contribute to the correct prediction of backorders

A Lightweight Convolutional Neural Network Architecture Applied for Bone Metastasis Classification in Nuclear Medicine: A Case Study on Prostate Cancer Patients

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