In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO₂ emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.

People with Alzheimer’s disease are at risk of malnutrition, overeating, and dehydration because short-term memory loss can lead to confusion. They need a caregiver to ensure they adhere to the main meals of the day and are properly hydrated. The purpose of this paper is to present an artificial intelligence system prototype based on deep learning algorithms aiming to help Alzheimer’s disease patients regain part of the normal individual comfort and independence. The proposed system uses artificial intelligence to recognize human activity in video, being able to identify the times when the monitored person is feeding or hydrating, reminding them using audio messages that they forgot to eat or drink or that they ate too much. It also allows for the remote supervision and management of the nutrition program by a caregiver. The paper includes the study, search, training, and use of models and algorithms specific to the field of deep learning applied to computer vision to classify images, detect objects in images, and recognize human activity video streams. This research shows that, even using standard computational hardware, neural networks’ training provided good predictive capabilities for the models (image classification 96%, object detection 74%, and activity analysis 78%), with the training performed in less than 48 h, while the resulting model deployed on the portable development board offered fast response times—that is, two seconds. Thus, the current study emphasizes the importance of artificial intelligence used in helping both people with Alzheimer’s disease and their caregivers, filling an empty slot in the smart assistance software domain.

Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.

We are going through a period in which the concept of the smart village (SV) is a novelty for the management of a community, and the new smart economy of the village is based on the power of community support. Appropriately, the development of a SV is related to a family’s participation in the motivation and access to education, the increase in knowledge of information technology, information and communications technology (ICT) literacy, and also in the creation of facilities for research and development (R&D). The partnership between the public administration, the private sector, and the community heads will lead to a smart economy within the village. At the same time, the intervention of the food system to support climate change can be supported by intelligent agriculture. The SV has a strong social significance; research in the field can be multidisciplinary, including human nutrition, climate change, and community education. This paper aims to X-ray the research areas of the SV from a multidisciplinary sense, in support of the partnership with the community, and to identify the main directions of strategic development. In total, 368 pieces of research on SVs from the last ten years were analyzed through bibliometric analysis using VOSviewer software, doubled by the co-occurrence of keywords and the bibliometric combination of documents, followed by a systematic review of the literature. The research undertaken was intended to contribute to the development of research for SVs, with the analysis of identified clusters. The results obtained will have a special contribution at the SV level through strategic and research proposals and suggest that the most important strategic and research directions for SVs focus on community education, its satiety, as well as several environmental and social changes generated by SVs.