The use of ontological models for intelligent systems construction allows for improved quality characteristics at all stages of the life cycle of a software product. The main source of improvement in quality characteristics is the possibility of reusing the conceptualization and code provided by the corresponding models. Due to the use of a single conceptualization when creating various software products, the degree of interoperability and code portability increases. The new-generation electronic business analytics systems implementation is based on the use of active models for business processes (BP). Such models, on the one hand, reflect the BPs taking place in the organization on a real-time scale, and on the other hand, embody corporate and other regulatory rules and restrictions and monitor their compliance. The purpose of this article is to research the methods of presenting and building active executable BP models, determining the methods of their execution and coordination, and building the resulting intelligent network of BP models. In the process of its implementation, such a network ensures the implementation, support of decision-making and compliance with regulatory rules in the relevant real BPs. A formal specification of an intelligent system for modelling a complex of BPs of the enterprise using models has been proposed. A hierarchical approach to the introduction of intelligent functions into the modelling system has been proposed. The simulation system is designed to be used for the design and management of complex intelligent systems. Achieving the set goal involves solving several development tasks: methods of presenting BP models for different types of such models; methods of analysis and display of time relations and attributes in BP models; ways of presenting the association of artefacts, and business analytics models with individual BP operations; metric ratios for evaluating the quality of process execution; methods of interaction of various BPs and coordination of their implementation. The purpose of functioning an intelligent model-driven software system is achieved through the interaction of a large number of simple models. At the same time, each model encapsulates a certain aspect of the expert's knowledge about the subject area. To apply executable conceptual models in the field of modelling BPes, it is necessary to determine the types of conceptual models used, their purpose and functions, and the role they play in the operation of an intelligent system. Models used in modelling BPes can be classified according to various characteristics. At the same time, the same model can be included in different classifications. 

In this paper, we take Industrial IoT (IloT) as a main point, where we touch on the direction of Industrial IoT concepts and connectivity protocols used by Industrial IoT devices. Moreover, we go into deep security challenges the Industrial ecosystem faces. Nowadays, most industries focus on specific protocols in their smart IoT devices. In return, we mainly focus on Message Queuing Telemetry Transport (MQTT) protocol, MQTT server where IoT devices are connected to it, and secure connectivity among server, cloud, and end user. Our purpose here is to describe the security approach for server and cloud-based environments and the utilization of cloud security tools such as IoT-hub, Network Security Group (NSG) and virtual private network (VPN). In more detail, here we have indicated proposed solution by separating into device, on-premises and cloud zone sections, proper technologies which are being used in modern security approaches and comparison with traditional IoT security approaches. This article enables readers to obtain fundamental knowledges on available technologies which are utilized in industrial areas and real-time scenarios where this solution is deployed.

Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG16 model, known for its robust performance in image classification, has been applied to the task of classifying pests in oil palm plants. This study aims to evaluate the effectiveness of the VGG16 model in identifying pests on oil palm, comparing the performance of default settings with models fine-tuned using grid search and random search techniques. We employed a quantitative approach, training the VGG16 model with three different configurations: default, fine-tuned with grid search, and fine-tuned with random search. Evaluation metrics including precision, recall, F1-Score, and overall accuracy were used to assess model performance across different pest categories: Metisa plana, Setora nitens, and Setothosea asigna. The default VGG16 model achieved precision, recall, and F1-Score values around 90% for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 91.00%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 93.88%, 92%, and 92.93% respectively, and an overall accuracy of 93%. The random search fine-tuning resulted in even higher performance, with precision of about 95.92%, recall of 94%, and F1-Score of 94.95% for Metisa plana, and overall accuracy of 94.67%. The VGG16 model demonstrated strong performance in pest classification on oil palm, with significant improvements achieved through fine-tuning techniques. The study confirms that grid search and random search fine-tuning can substantially enhance model accuracy and efficacy. Future research should focus on expanding the dataset to include more diverse pest species, incorporating attention mechanisms, and leveraging automated control technologies like drones and the Internet of Things (IoT) to further improve pest management practices.

This research explores the integration of artificial intelligence (AI), the internet of things (IoT), and smart technologies in sustainable development. The study identifies the applications of AI in waste management, smart cities, energy optimization, the green internet of things (GIoT), environmental resilience, pollution mitigation, and sustainable agriculture practices. The research emphasizes the need for a comprehensive approach to harness the potential of AI and IoT for sustainable development. The study also highlights the economic, social, and environmental dimensions of sustainable development and the implications of AI in these areas. The findings suggest that AI can contribute to inclusive and responsible economic growth, social equity and well-being, environmental conservation, and efficient resource utilization. The research provides valuable insights for researchers, practitioners, and policymakers working on sustainable development. 

The article proposes a method for clarifying the degree of correspondence between the request of a company looking for a specialist for a vacant position and the request of an applicant. As practice shows, the requirements in the requests of companies and the requests of applicants are linguistic, using vague boundaries of compliance parameters. Based on the results of a survey of graduate students and representatives of potential employer companies conducted by the author of the work, the company’s image, its experience in the market, and a decent salary are significant for graduates. For potential employers, applicants with some experience in the branch and low financial expectations are substantial first of all. Thus, from the statements of both sides, the author managed to identify two linguistic variables: “Time” and “Salary level.” These variables are applied to identify correlations between requests at the preliminary stage of staff selection. Indirect methods, namely methods of statistical data and expert assessments, were used in the variable membership functions. The algorithm for detecting matches is noticeably simple and can be in almost any table processor, which expands the field of the proposed approach. The recruitment agency acts as an intermediary in recruitment. The algorithm for identifying compliance in its activities will significantly reduce recruiters’ time and labor costs.

The increasing ubiquity of Social Internet of Things (SIoT) devices necessitates innovative data aggregation techniques to distill meaningful insights from diverse sources. This study introduces a Dynamic Data Aggregation Model for SIoT devices. The model aims to amalgamate static and mobile device data, employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for spatial clustering and Recurrent Neural Networks (RNN) for predicting mobile device movement patterns. The purpose is to offer a holistic approach to predictive analytics in the SIoT domain by seamlessly integrating these methodologies. The model begins with data preprocessing, ensuring data quality. It then applies DBSCAN for spatial clustering, enabling a comprehensive understanding of spatial relationships between devices. Simultaneously, RNNs are used for predictive modeling, specifically in forecasting mobile device movement patterns. The integration of DBSCAN clustering and RNNs forms the model’s innovative core, providing a unified solution for dynamic data aggregation. Comprehensive testing demonstrates the model’s notable accuracy in predicting mobile device movement patterns. By combining the spatial clustering capabilities of DBSCAN with the predictive power of RNNs, the model effectively unifies static and mobile data, advancing predictive analytics in the SIoT context. The proposed model yielded average values of 0.14604 (Mean Squared Error), 2.678385 (Mean Absolute Error), 0.307154 (Root Mean Squared Error), and 1.342317 (Mean Absolute Percentage Error), respectively. The Dynamic Data Aggregation Model proves its efficacy in addressing SIoT challenges. The integration of DBSCAN clustering and RNNs offers a versatile framework for dynamic data analysis, contributing significantly to predictive analytics in SIoT contexts.