Intelligent Support System Research Articles

An intelligent decision support system modelling for improving agroindustry's supply chain performance: a case study

An intelligent decision support system modelling for improving agroindustry's supply chain performance: a case study

A synergistic study on farmers’ income enhancement and urban-rural financial development under the perspective of common wealth by an intelligent decision support system for land improvement

Abstract This paper proposes the general framework and technical process of the intelligent decision support system for land improvement based on a multi-objective artificial immunity algorithm. Based on the analysis of the current situation of land use structure and spatial layout in County S, three land resource quantity structure optimization schemes are selected, and the improvement of agricultural land use efficiency and agricultural output value in the three schemes are evaluated and analyzed. The mechanisms that affect income distribution in urban-rural financial development and land finance are summarized. The results show that after land improvement, the area of cultivated land and grassland in County S decreased by 1,444 ha, and the area of garden land and forest land increased to the greatest extent in the economically prioritized scenario. The size of towns increased to 541 ha, and the size of villages decreased to different degrees and all three land improvement programs had the combined effect of increasing the gross agricultural product and boosting farmers’ incomes. The development of urban and rural finance is aided by land improvement, but the degree of inequality in income distribution is widened.

АВИАТЕХПОМ: СОСТОЯНИЕ И ПЕРСПЕКТИВЫ

This paper discusses the results of a project to create an intelligent decision support system for troubleshooting aircraft failures and malfunctions by the Aviation Technical Assistant (AviaTekhPom). The main implemented algorithms, functions, architecture and prototype of the system are described, as well as the further direction of the project development. The Sukhoi Superjet power supply system was chosen as the target object for testing solutions. The current version of the system provides processing of information presented in cards of accounting of malfunctions of aviation equipment and troubleshooting manuals, based on approaches known as rule-based reasoning and case-based reasoning. For preparation and modification of knowledge bases, a tabular formalism of knowledge representation is used, which provides the possibility of using publicly available tabular editors and CSV format. The developed algorithms are implemented in the form of conceptual prototypes of a desktop application and a chatbot.

An intelligent decision support system modelling for improving agroindustry's supply chain performance: a case study

An intelligent decision support system modelling for improving agroindustry's supply chain performance: a case study

Information system for improving the process of managing the manufacture of organic products

Climate change and increased anthropogenic impact on the environment, leading to environmental problems, pose a threat to all countries of the world and require the development of effective solutions in the field of organizing production processes, ensuring the use of resource-saving technologies, increasing production volumes through the use of progressive scientific achievements in the field of digitalization, ecology, chemistry, breeding, etc. As a result, the development of organic agriculture and an increase in its production volumes becomes relevant. The purpose of this paper is to develop a hybrid intelligent decision support system for assessing the effective production of organic crop production in regions, municipal districts, as well as on individual land plots, where data on natural and climatic characteristics, agrochemical soil analysis, monitoring of air parameters, etc. act as input parameters. Within the framework of the article, the authors focused on the consideration of the information, behavioral model and the model of the intellectual system components. Thanks to the implementation of these models of the system for improving the process of managing the manufacture of organic products, the user receives the most optimal proposed solutions on the issue of production, changing parameters and conditions characterizing the level of region development, climatic conditions, socio-economic situation, etc. at the entrance to the system.

The development of method for increasing the decision making efficiency in organizational and technical systems

The object of research is decision making processes in decision making support systems. The subject of the research is a method of decision making in management tasks using the walrus flock algorithm (WA), an advanced genetic algorithm and evolving artificial neural networks. A method of finding solutions using an improved walrus flock algorithm is proposed. The research is based on the walrus flock algorithm for finding a solution to the object’s condition. Evolving artificial neural networks are used to train walrus agents and an advanced genetic algorithm is used to select the best walrus agents. The method has the following sequence of actions: – input of initial data; – WA numbering in the flock; – determination of the initial speed of WA; – display of WA along the search plane; – preliminary assessment of the WA search area; – classification of food sources for WA; – sorting of the best WA individuals; – an update of WA positions; – WA migration; – checking the presence of a predator; – checking the stop criterion; – escape and struggle with predators; – checking the stop criterion; – training of WA knowledge bases; – determination of the amount of necessary computing resources, intelligent decision making support system. The originality of the proposed method lies in the placement of WA taking into account the uncertainty of the initial data, improved procedures of global and local edge taking into account the degree of noise of data on the state of organizational and technical systems. The use of the method makes it possible to increase the efficiency of data processing at the level 13–16 % due to the use of additional improved procedures. The proposed method should be used to solve problems of evaluating complex and dynamic processes

Development of a solution search method using the improved emperor penguin algorithm

The objects of the study are decision support systems. The subject of the study is the decision-making process in management problems using the Emperor Penguin Algorithm (EPA), an advanced genetic algorithm and evolving artificial neural networks. A solution search method using the improved EPA is proposed. The study is based on the EPA algorithm for finding a solution regarding the object state. Evolving artificial neural networks are used to train EPA, and an advanced genetic algorithm is used to select the best EPA. The method has the following sequence of actions: – input of initial data; – setting agents on the search plane; – numbering EPA in the flock; – setting the initial velocity of the EPA and thermal radiation of each EPA; – calculation of the position of each EPA on the total search area and its cost; – approach (attraction) of the EPA to another EPA; – changing in the trajectory of EPA movement; – selection of the best individuals from the EPA flock; – ranking the obtained solutions and sorting them; – training EPA knowledge bases; – determining the amount of necessary computing resources for an intelligent decision support system. The originality of the proposed method lies in setting EPA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the noise degree of data on the state of the analysis object. The method makes it possible to increase the efficiency of data processing at the level of 13–17 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

МОДЕЛИ И ФУНКЦИИ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ УПРАВЛЕНЧЕСКИХ РЕШЕНИЙ В ЭНЕРГОСИСТЕМАХ

The aim of the work is to study models and intelligent systems for supporting management decisions in energy systems. The article is devoted to solving the problem of adequate description of systems for supporting management decision-making in energy systems, as well as analysing models and functions of intelligent system models for supporting management decision-making in energy systems. As part of the research, methods of generalization and systematization of models, functions of an intelligent management decision support system, as well as systemic, evolutionary, categorical and modelling methods are used to form a conceptual model of the Transnistrian Moldavian Republic power systems which is necessary to further develop a mathematical model of intelligent system for supporting management decisions in energy systems. The novelty of the research lies in the fact that a conceptual model of a decision information system for the power supply control in the power systems of the Pridnestrovian Moldavian Republic is developed. The results of the study include analysing the subject area and developing a conceptual database model and the information support structure in the energy systems of the Transnistrian Moldavian Republic. Findings state that to develop an information system project, a system analysis of the energy industry, in particular information processes in the power supply is performed. The main aims and objectives of the project are determined, which become the basis for constructing an objective tree.

Sustainability-Driven Green Innovation: Revolutionising Aerospace Decision-Making with an Intelligent Decision Support System

Green innovation refers to developing and implementing new technologies, practices, products, and processes that promote sustainability and reduce environmental impacts. This article postulates the conceptualisation and implementation of an intelligent decision support system (IDSS) tailored to the aerospace technology sector. The data were collected from open sources such as social media and analyzed using the natural language processing tool. The envisaged IDSS is a comprehensive and seamlessly integrated platform designed to undergird decision-making, problem-solving, and research initiatives within the aerospace industry. Catering to the sector’s engineers, technicians, and managerial cadres, it aims to unravel complex datasets, proffer incisive analyses, and furnish prudent advice and recommendations. Its multifaceted capabilities range from data search and optimisation to modelling and forecasting. With an emphasis on harmonious integration with extant aerospace systems, it strives to provide engineers and technicians with enriched data insights. Moreover, its design ethos is centred on user-friendliness, underscored by an intuitive graphical interface that expedites seamless access and utilisation. Ultimately, the envisioned IDSS will augment the aerospace industry’s analytical prowess and will serve as a potent instrument for effective decision-making.

Development of a complex model for processing various data

The object of the research is intelligent decision-making support systems. The scientific problem that is solved in the research is the development of a comprehensive model for processing various types of data in intelligent decision-making support systems (DMSS). The relevance of the research lies in the fact that in intelligent DMSS circulate different in origin and units of measurement data obtained from various technical devices of obtaining information. The essence of the integrated approach in modeling is that two partial models are proposed: a model for processing different types of data in intelligent decision-making support systems and a model for processing homogeneous data in intelligent decision-making support systems. Analysis of the intelligent DMSS model for processing different types of data allows to draw such a conclusion. While solving the problem of processing different types of data, a model of intelligent DMSS is proposed, in contrast to traditional, even for the process of solving partial problems incorrectly started by experts with the help of self-organization, expressed in the coordination of partial tasks of the decision maker, striving for an ideal solution to the problem of processing different types of data, which increases the efficiency of finding an acceptable result for processing different types of data. The homogeneous data processing model is based on the idea that the same processing of homogeneous data in intelligent DMSS can be solved in parallel by different functional elements. Element integration relationships arise as internal non-verbal images in the user's memory, which can compare the dynamics of modeling a task for processing different types of data in intelligent DMSS from different points of view, which allows to see what modeling does not give using one model. The direction of further research should be considered the improvement of information processing methods in intelligent decision-making support systems.

Reinforcement learning-based DSS for coagulant and disinfectant dosage selection on drinking water treatment plants

Abstract The treatments to be applied for water purification must be dynamically adaptable to the raw water conditions. Currently, treatments are applied based on standards that are not optimized for the circumstances of each drinking water treatment plant (DWTP), neither for critical events. This paper presents a methodology for the creation of an Artificial Intelligence (AI) decision support system (DSS), encompassing the principal steps of the drinking water treatment processes (coagulation, sedimentation, filtration and disinfection), based on reinforcement learning techniques, that provides suggestions about the most efficient treatments (coagulant and chlorine dosages) for various raw water conditions, including critical events such as heavy rain and saline intrusions. Together with the model, a retraining strategy is included so the DSS adapts itself to the specific circumstances of each different DWTP. The model has been developed and validated in a DWTP replica. Furthermore, the model has been provided to a real DWTP to obtain feedback from experienced staff. The results and evaluation of the model are promising as a first approach on a DSS for drinking water treatments suggestion, although future versions might require more water quality parameters to characterize the raw water.

Medical decision support in the light of interactive granular computing: Lessons from the Ovufriend project

The main aim of the paper is to discuss the architecture for the future Intelligent Systems (IS's) and Decision Support Systems (DS's) dealing with complex phenomena such as supporting medical decisions (diagnosis and therapy) and to emphasize challenges in designing such systems. More precisely, the paper presents arguments for developing a specialized computing model based on the interactive granular computing paradigm which can help to design IS's and DS's more close to the prototypes of real life decision making. In this regard, the paper brings to the fore different experiences faced during designing other medical IS's or DS's.As a starting step, the paper considers the experience of developing the OvuFriend platform and outlines some possible extension of it in the framework of the proposed architecture on the basis of Interactive Granular Computing (IGrC) model. Specifically, our attempt is to analyze a scheme, which is being used in the platform of OvuFriend for determining health risks and possibilities of a woman to conceive a child, from the perspective of IGrC. The target of the paper is two fold. Firstly, to show how the underlying AI algorithm of this scheme can be related with the notion of computing in the context of IGrC. Secondly, to identify possible extensions of the existing scheme so that it becomes more dynamic, interactive, and close to personalized medicine.

Intelligent Decision Support System for Building Project Management Based on Artificial Intelligence

With the deepening of research on artificial intelligence (AI) technology in the field of computer science and the improvement of its social influence, the effective combination of AI technology and engineering project management practice has become a hot topic of discussion in the field of engineering project management. In the application research of AI in engineering project management, most of them use data-driven models to solve project management problems, improve data insight and improve the speed and quality of project management decision-making. Decision support systems have been developed on the basis of management information systems, but they have rarely been successfully applied in project management. The decision support system based on data warehouse is a new level of development in decision support systems, providing an effective way for projects to achieve real-time decision-making, thereby achieving synchronous, integrated, and optimized dynamic management of project progress, cost and function, and improving the timeliness and scientificity of decision-making. Therefore, this article proposes the theoretical framework and architecture of a construction project decision support system based on data warehousing. Then, this article proposes a deep belief network (DBN) evaluation method to evaluate the performance of the system, and the results show that the proposed method has superior performance.

Development and Evaluation of Intelligent Medical Decision Support Systems

This paper investigates the development and evaluation of intelligent medical decision support systems. Firstly, the current status of such systems is introduced, including technological advancements, clinical applications, and challenges faced. Subsequently, the overall framework design, key module design and implementation, system integration, and debugging are elaborated in detail. Finally, using the publicly available MIMIC medical dataset, a comprehensive evaluation and analysis of the system are conducted in a high-performance environment. The results indicate that the optimized system can achieve high accuracy and AUC. However, there is a slight decrease in effectiveness when dealing with rare diseases, and occasional false positives in certain high-risk situations. Overall, the research confirms the promising application prospects of such systems and provides a framework and path for future development.

Design and Development of an Intelligent Decision Support System for HR Recruitment

In the current era of globalized business environments, the effective management of human resources (HRM) plays a critical role in attracting and retaining skilled employees as companies expand into international markets. This study aims to tackle the challenges encountered in the HR recruitment process by designing and developing an intelligent decision support system based on the Mamdani fuzzy inference system. The proposed system harnesses the capabilities of fuzzy logic and advanced analytics to enhance the efficiency, accuracy, and efficacy of candidate selection. By automating tasks, the system streamlines the recruitment process and enhances candidate-job matching through the utilization of linguistic variables such as Technical Skillset, Experience, Certifications, Qualification, and Projects completed. The Mamdani fuzzy inference system facilitates adaptable decision-making based on imprecise or uncertain inputs, capturing expert knowledge and domain-specific heuristics. This approach optimizes the candidate ranking process, contributing to the successful acquisition of talented individuals and fostering organizational growth. By adopting this technology-driven HRM approach, organizations can surmount the limitations of manual methods, elevate their recruitment processes, and make more well-informed hiring decisions. The proposed Mamdani fuzzy inference system presents a robust and efficient solution that capitalizes on the potential of information technology to augment human resource management practices and gain a competitive edge in talent acquisition.

Intelligent decision support system for assessing the risk of failures of complex technical systems

A decision support system has been developed that allows assessing the risk of failure of elements and components of complex technical systems using artificial intelligence. The proposed algorithm for the functioning of a decision support system implements the task of automating the process of assessing the technical condition of complex systems. The use of the proposed decision support system for assessing the technical condition of complex systems will improve the reliability of operating systems with insufficient information about their technical condition.

Models and algorithms for the formation of an event tree used in analysing the quality of system software for knowledge-based intelligent decision support systems

A formal statement of the problem of improving the quality of software for intelligent systems under unfavourable circumstances is made. Its belonging to the class of problems of the calculus of variations on a conditional extremum is established. A ranked approach based on heuristic algorithms to solve this scientific problem is proposed and substantiated.

Pluvial Flood Risk Assessment in Urban Areas: A Case Study for the Archaeological Site of the Roman Agora, Athens

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

Intelligent Decision Support Systems—An Analysis of Machine Learning and Multicriteria Decision-Making Methods

Context: The selection and use of appropriate multi-criteria decision making (MCDM) methods for solving complex problems is one of the challenging issues faced by decision makers in the search for appropriate decisions. To address these challenges, MCDM methods have effectively been used in the areas of ICT, farming, business, and trade, for example. This study explores the integration of machine learning and MCDM methods, which has been used effectively in diverse application areas. Objective: The objective of the research is to critically analyze state-of-the-art research methods used in intelligent decision support systems and to further identify their application areas, the significance of decision support systems, and the methods, approaches, frameworks, or algorithms exploited to solve complex problems. The study provides insights for early-stage researchers to design more intelligent and cost-effective solutions for solving problems in various application domains. Method: To achieve the objective, literature from the years 2015 to early 2020 was searched and considered in the study based on quality assessment criteria. The selected relevant literature was studied to respond to the research questions proposed in this study. To find answers to the research questions, pertinent literature was analyzed to identify the application domains where decision support systems are exploited, the impact and significance of the contributions, and the algorithms, methods, and techniques which are exploited in various domains to solve decision-making problems. Results: Results of the study show that decision support systems are widely used as useful decision-making tools in various application domains. The research has collectively studied machine learning, artificial intelligence, and multi-criteria decision-making models used to provide efficient solutions to complex decision-making problems. In addition, the study delivers detailed insights into the use of AI, ML and MCDM methods to the early-stage researchers to start their research in the right direction and provide them with a clear roadmap of research. Hence, the development of Intelligent Decision Support Systems (IDSS) using machine learning (ML) and multicriteria decision-making (MCDM) can assist researchers to design and develop better decision support systems. These findings can help researchers in designing more robust, efficient, and effective multicriteria-based decision models, frameworks, techniques, and integrated solutions.

Artificial Intelligence for Biomass Detection, Production and Energy Usage in Rural Areas: A review of Technologies and Applications

Artificial intelligence, an emerging concept, has successfully been applied to bioenergy systems. However, highly scattered reviews were narrowly associated with either part of bioenergy systems or an isolated technique, and fewer focused on systematic induction in the agricultural context. This study reviewed 96 papers published from 2012 to 2022, focusing on generalising and comparing AI methods in agricultural bioenergy areas. Specifically, this review broke down the object of study of all previous studies into three parts: bioenergy systems, biomass materials, and AI techniques. Additionally, combined with examples of AI applications, it categorised the bioenergy systems into three phases, including (i) biomass feedstock detection, (ii) bioenergy production/process, and (iii) energy usage, for solving problems such as biomass mapping, composition analysis, cultivation monitoring, process optimisation, bioenergy planning, etc. Based on the review, 44 types of AI algorithms and 11 types of datasets were concluded, in which Artificial Neural Network, Random Forest, Support Vector Machine, Intelligence Decision Support System were mainly used for prediction, classification/regression, and optimal decision-making in issues about biomass systems with 58, 15, 13, and 13 algorithms, respectively.