Clinical support systems are affected by the issue of high variance in terms of chronic disorder prognosis. This uncertainty is one of the principal causes for the demise of large populations around the world suffering from some fatal diseases such as chronic kidney disease (CKD). Due to this reason, the diagnosis of this disease is of great concern for healthcare systems. In such a case, machine learning can be used as an effective tool to reduce the randomness in clinical decision making. Conventional methods for the detection of chronic kidney disease are not always accurate because of their high degree of dependency on several sets of biological attributes. Machine learning is the process of training a machine using a vast collection of historical data for the purpose of intelligent classification. This work aims at developing a machine-learning model that can use a publicly available data to forecast the occurrence of chronic kidney disease. A set of data preprocessing steps were performed on this dataset in order to construct a generic model. This set of steps includes the appropriate imputation of missing data points, along with the balancing of data using the SMOTE algorithm and the scaling of the features. A statistical technique, namely, the chi-squared test, is used for the extraction of the least-required set of adequate and highly correlated features to the output. For the model training, a stack of supervised-learning techniques is used for the development of a robust machine-learning model. Out of all the applied learning techniques, support vector machine (SVM) and random forest (RF) achieved the lowest false-negative rates and test accuracy, equal to $99.33\%$ and $98.67\%$, respectively. However, SVM achieved better results than RF did when validated with 10-fold cross-validation.

Feature selection (FS) is commonly thought of as a pre-processing strategy for determining the best subset of characteristics from a given collection of features. Here, a novel discrete artificial gorilla troop optimization (DAGTO) technique is introduced for the first time to handle FS tasks in the healthcare sector. Depending on the number and type of objective functions, four variants of the proposed method are implemented in this article, namely: (1) single-objective (SO-DAGTO), (2) bi-objective (wrapper) (MO-DAGTO1), (3) bi-objective (filter wrapper hybrid) (MO-DAGTO2), and (4) tri-objective (filter wrapper hybrid) (MO-DAGTO3) for identifying relevant features in diagnosing a particular disease. We provide an outstanding gorilla initialization strategy based on the label mutual information (MI) with the aim of increasing population variety and accelerate convergence. To verify the performance of the presented methods, ten medical datasets are taken into consideration, which are of variable dimensions. A comparison is also implemented between the best of the four suggested approaches (MO-DAGTO2) and four established multi-objective FS strategies, and it is statistically proven to be the superior one. Finally, a case study with COVID-19 samples is performed to extract the critical factors related to it and to demonstrate how this method is fruitful in real-world applications.

Rural areas are significantly affected by spatial vulnerability, the digital gap, depopulation, and population ageing. Marginalized populations are seeking collective well-being, social inclusion, and local development in smart villages, an increasingly important area of interest for scholars and practitioners as well as rural areas and communities. This article attempts to highlight the dominant trends in smart villages planning and depict the characteristics of Greek rural areas and populations alongside the implemented localized smart actions. To achieve this aim, the research utilized the existing literature through bibliometric analysis by extracting data from the Web of Science database. Building upon the bibliometrics, the research focused on identifying localized implemented interventions in the Greek rural areas. The results suggest that innovation, knowledge, growth, and management appear to impact rural smart planning, while the limited interventions of smart villages in Greece focus on social innovation and local development. The study argues that in Greece, a single holistic smart villages model cannot be proposed, due to the country’s geographical and demographical variability. The proposed trends, though, can be implemented locally to encourage rural development and population inclusion; therefore it is recommended to increase local stakeholders’ awareness and active engagement.

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the environment. To address this problem, a Smart Waste Management System is introduced in this paper, employing machine learning techniques for air quality level classification. Furthermore, this system safeguards garbage collectors from severe health issues caused by inhaling harmful gases emitted from the waste. The proposed system not only proves cost-effective but also enhances waste management productivity by categorizing waste into three types: wet, dry, and metallic. Ultimately, by leveraging machine learning techniques, we can classify air quality levels and garbage weight into distinct categories. This system is beneficial for improving the well-being of individuals residing in close proximity to dustbins, as it enables constant monitoring and reporting of air quality to relevant city authorities.

The Greek National Energy and Climate Plan was validated by the Greek Governmental Committee of Economic Policy on 23 December 2019. The decisions included in this plan will have a significant impact on the Greek energy mix as the production of electricity from lignite combustion ceases in 2028, when lignite will be replaced by natural gas (NG) and renewable energy sources (RES). This work presents an assessment of the Greek National Energy and Climate Plan by analyzing its pros and cons. The main critiques made are focused on the absence of risk analysis and alternative scenarios, the proposed energy mix, the absence of other alternatives on the energy mix and energy storage, the low attention given to energy savings (transport, buildings), the future energy prices, and the economic and social impacts. This analysis shows that delaying this transition for some years, to better prepare it by taking into consideration the most sustainable paths for that transition, such as using more alternatives, is the best available option today.

Innovative procurement is an important tool for smart cities to improve the effectiveness and efficiency of public services, especially in sectors such as smart living (for example, health conditions), smart mobility, or smart environment (with emphasis on waste and water management). The European Union (EU) public procurement legislative framework encourages the deployment of innovation by several means (including, inter alia, the introduction of competitive procedures promoting innovation, use of award criteria based on factors other than price, and the life-cycle approach) and sets the scene for a more strategic procurement for EU smart cities. Despite the proven benefits of innovative procurement, public authorities, driven mainly by their preference to follow traditional tender procedures under solely budgetary considerations, have hesitated to introduce innovation. The case study of Greece is examined, and it is concluded that despite the adopted policy measures that are conducive for mainstreaming innovation procurement, innovation procurement in Greece is at an early development stage. One of the reasons that hinder the application of innovation-oriented procedures by public purchasers is their insufficient knowledge of the available legal framework. The broad objective of this article is to outline the main innovation-friendly tools, as set out in the applicable European public procurement legislative framework that smart cities should adopt in order to make strategic use of innovative procurement.