Analyzing Relationship between Financing Constraints, Entrepreneurship, and Agricultural Company Using AI-Based Decision Support System

Decision support technology has become a key link in modern information strategy. With the deepening of research, introduced expert systems have been introduced into decision support systems. In this way, decision support systems gradually become more uncertain and capable of handling uncertainties. The development direction of decision support system is typically based on qualitative analysis. Intelligent decision support system is a system that combines decision support system with artificial intelligence technology. This study attempts to assess in an innovative way the relationship between financing constraints, entrepreneurship, and agricultural firms. The most recently proposed intelligent decision support system, AI-assisted Intelligent Decision Support System (AIIDSS), is used to predict the impact of entrepreneurship on corporate performance. The paper constructs an entrepreneurship index from five aspects: innovation, competitiveness, human capital accumulation, management capability, and adventurous spirit. The method intends to construct the Kaplan–Zingales (KZ) index to evaluate financing constraints. Through an empirical study, it was found that entrepreneurship can significantly promote the growth of listed agricultural companies. The study can drastically reduce the difficulties involved in financing constraints normally faced by agricultural companies. The impact paths include increasing agricultural company operating cash flow, improving stock liquidity, and increasing debt financing. The research suggests that if listed agricultural companies are to improve financing constraints, entrepreneurs must improve their own competitiveness and management capabilities. This will help in reasonably controlling research and development investment besides the impulse to take risks. As the growth of an enterprise relies on considering the determinants of financing constraints, this research provides an effective investigation technique. Moreover, the findings of the study will help entrepreneurs, particularly agricultural companies, to bear most of the risks and to avail most of the opportunities.

Construction of Smart City Street Landscape Big Data-Driven Intelligent System Based on Industry 4.0

With the application of engineering management in smart city construction under Industry 4.0, the intelligent design of urban street landscape has attracted extensive attention. Affected by the low intelligent level of traditional landscape design, the existing urban landscape composite system has difficulty in meeting the needs of smart city construction. Therefore, this paper proposes the construction of street landscape big data-driven intelligent decision support system based on Industry 4.0. Based on the complex network theory, this paper analyzes the structure, links, nodes, driving forces, and functional requirements of urban street landscape and then puts forward the construction content and implementation method of urban street landscape intelligent decision support system. The system consists of four aspects: intelligent infrastructure, service, protection and maintenance, and management and evaluation system. Its implementation not only reflects the cooperation and effective application of intelligent technology in each stage of street landscape construction, but also provides reference for the application of engineering management in other fields under Industry 4.0.

THE CONCEPT OF CREATING AN INTELLECTUAL CORE OF AN INTEGRATED INFORMATION AND ANALYTICAL SYSTEM FOR ACTION IN EMERGENCIES OF MAN-MADE NATURE

Subject matter. Informatization of processes of counteraction to man-made emergencies. Goal. Improving the effectiveness of the process of information support for decision-making in overcoming the consequences of man-made emergencies, in terms of its intellectualization, by creating a concept of integration of various software tools within a single, homogeneous space of knowledge about strategic, tactical and operational actions in a wide range of accidents and disasters related to the operation of technical facilities. Tasks. Develop a formal statement of the problem of decision-making in an emergency situation and justify the methodology of its implementation based on the integration of knowledge tools with various analytical models that describe the processes of emergency. Consider the application of the proposed methodology on a scenario that reproduces the situation on the site after the leakage of a highly toxic substance. Methods. System analysis - in the development of a comprehensive process model for decision-making in an emergency; software engineering - when creating the architecture of an integrated intelligent decision support system in emergencies of man-made nature; physics of the processes of distribution of toxic chemicals in the atmosphere - in the development of a scenario of the situation at the site in an emergency situation at an industrial site. Results. The concept of creating an integrated intelligent decision support system for overcoming the consequences of man-made emergencies, in particular, the formal formulation of a typical problem of decision-making in emergencies is described, as well as the basic principles of its solution, and the formulation of the problem of modeling management processes associated with man-made emergencies is developed. Conclusions. The concept of creating an integrated information-analytical system to support decision-making in man-made emergencies is presented. Within the framework of this concept, formal models of decision-making in man-made emergencies and an approach to the integration of diverse software within a single, homogeneous knowledge space on comprehensive measures to overcome the consequences of emergencies related to accidents and disasters at technical facilities infrastructure. The scenario example of the organization of intellectual support of decisions at release into the atmosphere of a toxic chemical is considered.

Intelligent Decision Support System

A phishing attack is one of the most common forms of cybercrime worldwide. In recent years, phishing attacks have continued to escalate in severity, frequency and impact. Globally, the attacks cause billions of dollars of losses each year. Cybercriminals use phishing for various illicit activities such as personal identity theft and fraud, and to perpetrate sophisticated corporate-level attacks against financial institutions, healthcare providers, government agencies and businesses. Several solutions using various methodologies have been proposed in the literature to counter web-phishing threats. This research work adopts a novel strategy to the detection and prevention of website phishing attacks, with a practical implementation through development towards a browser toolbar add-in. The IPDS is shown to be highly effective both in the detection of phishing attacks and in the identification of fake websites. Experimental results show that approach using the CNN + LSTM has a 93.28% accuracy with an average detection time of 25 seconds, whilst the approach has a slightly lower accuracy. These times are within typical times for loading a web page which makes toolbar integration into a browser a practical option for website phishing detection in real time. The results of this development are compared with previous work and demonstrate both better or similar detection performance. This is the first work that considers how best to integrate images, text and frames in a hybrid feature-based solution for a phishing detection scheme.