The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

Hyperparameter optimization is a fundamental part of Auto Machine Learning (AutoML) and it has been widely researched in recent years; however, it still remains as one of the main challenges in this area. Motivated by the need of faster and more accurate hyperparameter optimization algorithms we developed HyperBRKGA, a new population-based approach for hyperparameter optimization. HyperBRKGA combines the Biased Random Key Genetic Algorithm with an Exploitation Method in order to search the hyperparameter space more efficiently than other commonly used hyperparameter optimization algorithms, such as Grid Search, Random Search, CMA-ES or Bayesian Optimization. We develop and test two different alternatives for this Exploitation Method: Random Walk and Bayesian Walk. We also discuss and implement other schemes, such as a Training Data Reduction Strategy and a Diversity Control strategy, in order to further improve the efficacy of our method. We performed several computational experiments on 8 different datasets to assess the effectiveness of the proposed approach. Results showed that HyperBRKGA could find hyperparameter configurations that outperformed in terms of predictive quality the baseline methods in 6 out of 8 datasets while showing a reasonable execution time. Lastly, we conducted an ablation study and showed that the addition of every component was relevant to achieving high quality results.

To mitigate the effects of the lack of IoT standardization, including scalability, reusability, and interoperability, we propose a domain-agnostic monitoring and control framework (MCF) for the design and implementation of Internet of Things (IoT) systems. We created building blocks for the layers of the five-layer IoT architecture and built the MCF’s subsystems (monitoring subsystem, control subsystem, and computing subsystem). We demonstrated the utilization of MCF in a real-world use-case in smart agriculture, using off-the-shelf sensors and actuators and an open-source code. As a user guide, we discuss the necessary considerations for each subsystem and evaluate our framework in terms of its scalability, reusability, and interoperability (issues that are often overlooked during development). Aside from the freedom to choose the hardware used to build complete open-source IoT solutions, the MCF use-case was less expensive, as revealed by a cost analysis that compared the cost of implementing the system using the MCF to obtain commercial solutions. Our MCF is shown to cost up to 20 times less than normal solutions, while serving its purpose. We believe that the MCF eliminated the domain restriction found in many IoT frameworks and serves as a first step toward IoT standardization. Our framework was shown to be stable in real-world applications, with the code not incurring a significant increase in power utilization, and could be operated using common rechargeable batteries and a solar panel. In fact, our code consumed so little power that the usual amount of energy was two times higher than what is necessary to keep the batteries full. We also show that the data provided by our framework are reliable through the use of multiple different sensors operating in parallel and sending similar data at a stable rate, without significant differences between the readings. Lastly, the elements of our framework can exchange data in a stable way with very few package losses, being able to read over 1.5 million data points in the course of three months.

In this paper, we want to study the informative value of negative links in signed complex networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the European Parliament (EP). We first process some data collected by the Vote Watch Europe Website for the whole 7th term (2009-2014), by considering voting similarities between Members of the EP to define weighted signed links. We then apply a selection of community detection algorithms, designed to process only positive links, to these data. We also apply Parallel Iterative Local Search (Parallel ILS), an algorithm recently proposed to identify balanced partitions in signed networks. Our results show that, contrary to the conclusions of a previous study focusing on other data, the partitions detected by ignoring or considering the negative links are indeed remarkably different for these networks. The relevance of negative links for graph partitioning therefore is an open question which should be further explored.

Cyberbullying is one of the harmful activities on social networks that particularly affects the mental well-being of adolescents. Recent research has focused on session-based approaches to cyberbullying detection, which consider various components of a social media session, including posts, comments, and user information. This holistic perspective deepens the understanding of cyberbullying. However, the effective modeling of interactions within a session by incorporating session components remains a significant challenge. Previous methodologies have often failed to capture the complex dependencies between session components, thereby limiting their efficacy in modeling session interactions within a graph structure. Consequently, critical patterns and insights within sessions may be overlooked. To address this problem, we initially modeled social media sessions and their interactions as heterogeneous graphs. Subsequently, we introduced a heterogeneous graph neural network framework for session-based cyberbullying detection. Our approach leverages a Heterogeneous Graph Neural Network to capture complex high-order semantic relationships from a learned heterogeneous graph. The model comprises three components: a feature encoder, a heterogeneous graph encoder, and a classifier that collaboratively classifies social media sessions as bullying or not. We conducted experiments using two real-world session-based datasets to assess the effectiveness of the proposed method. The results demonstrate remarkable performance on the Vine dataset, achieving an F1-score of 99.34. In addition, our method provided competitive results for the Instagram dataset. Furthermore, we carried out extensive experiments to underscore the significance of the graph components in the proposed approach.