Abstract: Precision agriculture, which involves the use of information systems in agriculture, is becoming increasingly popular as it helps ensure optimal crop yields while maintaining plant health and protection. Our aim is to address the need for a comprehensive monitoring system for perennial plantations by developing a software solution that processes images and provides actionable insights into plant health and growth. The proposed system is an idea for a software solution based on artificial intelligence. In order to generate information about plants, a system using object detection and image segmentation approaches is proposed. Ultimately, our goal is to create a system that empowers farmers and agricultural professionals to make data-driven decisions about plantation management, leading to improved crop yields and sustainability.

Abstract: Ransomware is malicious software that encrypts or blocks access to data or computer systems, demanding a ransom for decryption or reestablishment of access. Ransomware attacks have become more sophisticated, using a variety of distribution techniques, including phishing emails, malicious ads, and exploiting vulnerabilities in computer systems. This type of attack can have serious consequences, including financial loss, damage to the reputation of organizations and violation of user privacy. This scientific paper gives a comprehensive overview of ransomware attacks, analyzes their characteristics and consequences, and explores the various defense mechanisms that are used to prevent, detect, and respond to these threats. Defense mechanisms against ransomware attacks include prevention, detection and response. Prevention focuses on implementing best practices such as employee training, system updates, and implementing security protocols. Detection involves the use of advanced tools to detect the presence of ransomware, while response involves isolating infected systems, notifying authorities and recovering data. The paper also shows examples of successful defense mechanisms that have been applied in practice. Advances in ransomware attack research and defense mechanisms are crucial for developing more effective protection strategies. Understanding the characteristics and tactics of attackers makes it possible to adjust defensive strategies, while identifying best practices contributes to improving the overall security of the digital environment. The aim of this paper is to provide a thorough overview of ransomware attacks and defense mechanisms, contributing to improving the security of organizations and individuals in the face of increasingly complex cyber threats.

Abstract: The aim of this study is to assess and evaluate different machine learning techniques, in synergy with deep learning, for predicting immersion in virtual reality. Python modules, combined with descriptive statistical tools and nntraintool in Matlab environment are used to accomplish this task. Analysis is conducted over the set of 401 cases with 23 variables. Linear, lasso, lassoCV, ridge, and Bayesian regression are tested for different sets of input data. These input data consist of 22 or 8 independent variables. The best score (0.4419295) is obtained for lasso CV model. In order to improve the results Artificial Neural Networks (ANNs) with architecture types 22-n-1, and 8-n-1, are tested. Number of neurons in hidden layer is marked with n (n = 5, 10, 20, 30, 40, 50), 22 and eight are numbers of neurons in input layer, while one stands for the number of neurons in the output layer and corresponds to the level of immersion. The ANNs are trained with Levenberg - Marquardt (LM) algorithm, and Scaled Conjugated Gradient (SCG) algorithm. The results are evaluated according to the criteria of the highest value of correlation coefficient (R) for train data and the lowest values of Mean Squared Errors. These results are cross-referenced with the best performances of proposed ANNs. Architecture 8-5-1, trained with SCG algorithm, is proven to be the best solution, with R-value for train data equal to 0.726, and the value of Mean Square Error (MSE) equal to 0.620. Best validation performance is 0.689, reached at epoch 18.

Abstract: This paper analyzes the public transportation network of Belgrade using techniques from the field of complex network analysis. Using publicly available data about the stops and routes, we built two network models of the Belgrade transportation network. The first model is based on the often-used L-space model and serves as a benchmark for the second, spatially augmented model. Spatial augmentation is done with the use of a novel station aggregation algorithm we proposed, named Spatial Coordinate-based Agglomerative Node Aggregation (SCANA). SCANA aims to optimize transit systems by identifying and grouping spatially proximate stations. The study leverages geographical and connectivity data from Belgrade’s public transportation system, employing SCANA to identify clusters of stations with high spatial coherence. The algorithm’s application facilitates the creation of aggregated station groups, enabling a more efficient representation of the complex network structure. To evaluate the benefits of spatial augmentation with the SCANA algorithm, we compare global network metrics, inspect the top nodes based on centrality measures, and explore the community structure of the two network models. We show that the spatially augmented network model demonstrates measurable improvements regarding network connectivity and ease of transport: a decrease in average path length of 11.5% and a decrease in network diameter of 13.2%. The augmented network exhibits a 2.6 times higher average clustering coefficient, and the community structure expressed by the spatially augmented model is more coherent and adheres better to the geographical and organizational constraints of the city.

Abstract: In this comprehensive scientific manuscript, the intricate process of developing and deploying cutting-edge alarms and warning systems tailored for the precise detection of physico-chemical pollution within river ecosystems is investigated. Through meticulous investigation, a spectrum of methodologies and technologies is thoroughly scrutinized, unraveling their intricate interplay and practical applications in the realm of environmental monitoring. The overarching goal is to provide nuanced and valuable insights, advancing the collective understanding of how pollution in river environments can be effectively detected and responded to. An in-depth analysis of early warning systems is offered, elucidating their potential to tackle the intricate challenges posed by physico-chemical pollution in river ecosystems. By exploring the diverse applications of these systems, substantive contributions are made to the discourse on sustainable management and preservation of critical freshwater ecosystems. Through this detailed examination, the current landscape of environmental monitoring is not only illuminated but also the way is paved for informed decision-making and strategic interventions to safeguard the health and sustainability of vital water resources.

Abstract: In recent years, there has been a notable shift towards the use of deep learning methodologies in forecasting with promising results. Specifically, models such as Long Short-Term Memory (LSTM) networks and Temporal Convolution Networks (CNNs) have demonstrated significant potential in this domain. However, the black-box nature of neural networks poses a substantial hurdle to their widespread adoption, primarily due to the lack of understanding and trust by the end users. Providing explanations of the model’s prediction should increase the trust in the system and make the peculiar decisions easily examined. This paper presents a survey of machine learning systems for time series prediction based on neural networks, augmented with eXplainable AI (XAI) techniques. A architecture integrating LSTM neural networks with SHAP (SHapley Additive exPlanations) values is proposed. The XAI module generates explanations for model predictions, which are subsequently presented to users alongside prediction results, allowing them to understand the system’s decisions. Furthermore, additional benefit of XAI module is the possibility to experiment with different prediction models and compare input feature effects.