Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

Smart cities are urban areas that utilize digital solutions to enhance the efficiency of conventional networks and services for sustainable growth, optimized resource management, and the well-being of its residents. Today, with the increase in urban populations worldwide, their importance is greater than ever before and, as a result, they are being rapidly developed to meet the varying needs of their inhabitants. The Internet of Things (IoT) lies at the heart of such efforts, as it allows for large amounts of data to be collected and subsequently used in intelligent ways that contribute to smart city goals. Time-series forecasting using deep learning has been a major research focus due to its significance in many real-world applications in key sectors, such as medicine, climate, retail, finance, and more. This review focuses on describing the most prominent deep learning time-series forecasting methods and their application to six smart city domains, and more specifically, on problems of a multivariate nature, where more than one IoT time series is involved.

Carbon Dioxide (CO $$_{2}$$2 ) is a significant contributor to greenhouse gas emissions and one of the main drivers behind global warming and climate change. In spite of the global economic slowdown due to the COVID-19 pandemic, the global average atmospheric CO $$_{2}$$2 concentration reached a new record high in 2020 with its year-on-year increase being the fifth highest annual increase in 63 years, according to the National Oceanic and Atmospheric Administration. Furthermore, the years 2020 and 2019 were respectively the second and third warmest, while the decade 2010–2019 was the warmest decade ever recorded. In an attempt to curb this climate emergency, many countries and organizations globally have adopted ambitious goals and announced plans to help dramatically reduce CO $$_{2}$$2 emissions. As part of these plans, various innovative smart city projects are being developed, focusing on implementing Internet of Things (IoT) technologies. By collecting sensor-based data, such technologies aim towards automating data-driven decision-making around carbon emission management and reduction. In this work, a hybrid machine learning system, aimed at forecasting CO $$_2$$2 concentration levels in a smart city environment was developed using a multivariate time series dataset containing IoT sensor measurements of CO $$_{2}$$2 , as well as various environmental factors, taken at every second. The proposed system demonstrated superior performance to similar methods, while also maintaining a high degree of interpretability. More specifically, the approach was empirically compared against other similar approaches in several scenarios and use cases, thus also offering more insight into the predictive capabilities of such state-of-the-art systems. For this comparison, both traditional time series and deep learning approaches were employed, including the current state-of-the-art architectures, such as attention-based, transformer networks. Results demonstrated that, when measured across various settings and metrics, including three different forecasting horizons, the hybrid solution achieved the best overall results, and in some cases, the difference in performance was statistically significant. At the same time, insights from the system’s inner workings were extracted, shedding light on the reasoning behind the model’s predictions and the factors that contribute to them, thus showcasing its transparency. Lastly, throughout the experiments, deep learning approaches illustrated their ability to better handle the multivariate nature of the dataset and in general tended to outperform the traditional time series methods, especially for longer forecasting horizons.

Outlier detection refers to the problem of the identification and, where appropriate, the elimination of anomalous observations from data. Such anomalous observations can emerge due to a variety of reasons, including human or mechanical errors, fraudulent behaviour as well as environmental or systematic changes, occurring either naturally or purposefully. The accurate and timely detection of deviant observations allows for the early identification of potentially extensive problems, such as fraud or system failures, before they escalate. Several unsupervised outlier detection methods have been developed; however, there is no single best algorithm or family of algorithms, as typically each relies on a measure of ‘outlierness’ such as density or distance, ignoring other measures. To add to that, in an unsupervised setting, the absence of ground-truth labels makes finding a single best algorithm an impossible feat even for a single given dataset. In this study, a new meta-learning algorithm for unsupervised outlier detection is introduced in order to mitigate this problem. The proposed algorithm, in a fully unsupervised manner, attempts not only to combine the best of many worlds from the existing techniques through ensemble voting but also mitigate any undesired shortcomings by employing an unsupervised feature selection strategy in order to identify the most informative algorithms for a given dataset. The proposed methodology was evaluated extensively through experimentation, where it was benchmarked and compared against a wide range of commonly-used techniques for outlier detection. Results obtained using a variety of widely accepted datasets demonstrated its usefulness and its state-of-the-art results as it topped the Friedman ranking test for both the area under receiver operating characteristic (ROC) curve and precision metrics when averaged over five independent trials.