The concept of circular economy (CE) is becoming progressively popular with academia, industry, and policymakers, as a potential path towards a more sustainable economic system. Information and communication technology (ICT) systems have influenced every aspect of modern life and the CE is no exception. Cutting-edge technologies, such as big data, cloud computing, cyber-physical systems, internet of things, virtual and augmented reality, and blockchain, can play an integral role in the embracing of CE concepts and the rollout of CE programs by governments, organizations, and society as a whole. The current paper conducts an extensive academic literature review on prominent ICT solutions paving the way towards a CE. For the categorization of the solutions, a novel two-fold approach is introduced, focusing on both the technological aspect of the solutions (e.g., communications, computing, data analysis, etc.), and the main CE concept(s) employed (i.e., reduce, reuse, recycle and restore) that each solution is the most relevant to. The role of each solution in the transition to CE is highlighted. Results suggest that ICT solutions related to data collection and data analysis, and in particular to the internet of things, blockchain, digital platforms, artificial intelligence algorithms, and software tools, are amongst the most popular solutions proposed by academic researchers. Results also suggest that greater emphasis is placed on the “reduce” component of the CE, although ICT solutions for the other “R” components, as well as holistic ICT-based solutions, do exist as well. Specific important challenges impeding the adoption of ICT solutions for the CE are also identified and reviewed, with consumer and business attitude, economic costs, possible environmental impacts, lack of education around the CE, and lack of familiarization with modern technologies being found among the most prominent ones.

The upcoming agricultural revolution, known as Agriculture 4.0, integrates cutting-edge Information and Communication Technologies in existing operations. Various cyber threats related to the aforementioned integration have attracted increasing interest from security researchers. Network traffic analysis and classification based on Machine Learning (ML) methodologies can play a vital role in tackling such threats. Towards this direction, this research work presents and evaluates different ML classifiers for network traffic classification, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF) and Stochastic Gradient Descent (SGD), as well as a hard voting and a soft voting ensemble model of these classifiers. In the context of this research work, three variations of the NSL-KDD dataset were utilized, i.e., initial dataset, undersampled dataset and oversampled dataset. The performance of the individual ML algorithms was evaluated in all three dataset variations and was compared to the performance of the voting ensemble methods. In most cases, both the hard and the soft voting models were found to perform better in terms of accuracy compared to the individual models.

The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variability of crops. Information and communication technology (ICT) systems have influenced and shaped every part of modern life, and PA is no exception. The current paper conducts a literature review of prominent ICT solutions, focusing on their role in supporting different phases of the lifecycle of PA-related data. In addition to this, a data lifecycle model was developed as part of a novel categorization approach for the analyzed solutions.

Data scarcity in the healthcare domain is a major drawback for most state-of-the-art technologies engaging artificial intelligence. The unavailability of quality data due to both the difficulty to gather and label them as well as due to their sensitive nature create a breeding ground for data augmentation solutions. Parkinson’s Disease (PD) which can have a wide range of symptoms including motor impairments consists of a very challenging case for quality data acquisition. Generative Adversarial Networks (GANs) can help alleviate such data availability issues. In this light, this study focuses on a data augmentation solution engaging Generative Adversarial Networks (GANs) using a freezing of gait (FoG) symptom dataset as input. The data generated by the so-called FoGGAN architecture presented in this study are almost identical to the original as concluded by a variety of similarity metrics. This highlights the significance of such solutions as they can provide credible synthetically generated data which can be utilized as training dataset inputs to AI applications. Additionally, a DNN classifier’s performance is evaluated using three different evaluation datasets and the accuracy results were quite encouraging, highlighting that the FOGGAN solution could lead to the alleviation of the data shortage matter.

Over the last few years, Big Data applications have attracted ever-increasing attention in several scientific and business domains. Biomedicine, transportation, entertainment, and aerospace are only a few examples of sectors which are increasingly dependent on applications, where knowledge is extracted from huge volumes of heterogeneous data. The main goal of this paper was to conduct an academic literature review of prominent publications revolving around the application of BD in aerospace. A total of 67 publications were analyzed, highlighting the sources, uses, and benefits of BD. For categorizing the publications, a novel 6-fold approach was introduced including applications in aviation technology and aviation management, UAV-enabled applications, applications in military aviation, health/environment-related applications, and applications in space technology. Aiming to provide the reader with a clear overview of the existing solutions, a total of 15 subcategories were also utilized. The results indicated numerous benefits deriving from the application of BD in aerospace. These benefits referred to the aerospace domain itself as well as to many other sectors including healthcare, environment, humanitarian operations, network communications, etc. Various data sources and different Machine Learning models were utilized in the analyzed publications and the use of BD-based techniques enabled us to extract useful correlations and gain useful insights from large volumes of data.

The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.