Expert Decision Support System Research Articles

О методах технического анализа, применяемых в торговых роботах частных экспертных систем поддержки принятия решений

О методах технического анализа, применяемых в торговых роботах частных экспертных систем поддержки принятия решений

Complexity and the Sustainable Development Goals: A Computational Intelligence Approach to Support Policy Mix Designs

In this article, a 3-step neuro-fuzzy expert decision support system is constructed in order to investigate the multifaceted performance interdependencies among 17 SDG performance scores across 162 UN Member States. The direct influence matrix among 17 SDGs, which would be filled by policy experts in interpretive structural modeling, is instead populated by computational intelligence. Results indicate that, the most influential performance drivers are SDG12 (Sustainable Production and Consumption), SDG 9 (Industry, Innovation and Infrastructure) and SDG 11 (Sustainable Cities and Communities) at global level. Yet these findings highlight the importance of establishing and enhancing local infrastructures and communities, innovative and sustainable supply and demand content to increase overall SDGs performance globally. Performance linkages SDG 5 (Gender Equality) and SDG 13 (Climate Action) are global common denominators across localities for positive evolution of overall SDGs performance. Local policy mixes between performance driver and linkage SDGs are recommended by taking eight dependent SDG performances (SDG 10, 16, 15, 8, 6, 17, 7, 2) into account as action contexts. Four autonomous (less influential) SDG performances (SDG 1, 3, 4, 14) remain to be integrated. Conclusions call for a global unity in diversity, local policy mixes by all cities and communities around the globe.

Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW

The vehicle routing problem with time windows (VRPTW) is an important and widely studied combinatorial optimization problems. This paper aims at VRPTW with the objectives of reducing the number of vehicles and minimizing the time-wasting during the delivery process caused by early arrival. To solve this NP-hard problem, a hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search (HMOEA-GL) is proposed. Firstly, fast sampling strategy-based global search (FSS-GS) of HMOEA-GL extensively explores the entire solution space to quickly guide the search direction towards the center and edge areas of Pareto frontier. Secondly, route sequence difference-based local search (RSD-LS) is executed on the individuals with poor performance in the population obtained by FSS-GS to enhance the search ability of HMOEA-GL. In addition, the suitable coding method and proper genetic operators are designed, especially, a simple insertion search is used to reduce the number of vehicles in VRPTW. Comparing with NSGA-II, SPEA2, and MOEA/D, experimental results on 12 Solomon benchmark test problems indicate that the proposed HMOEA-GL is effective, and more excellent in convergence, while maintaining a satisfying distribution performance. HMOEA-GL could be an effective intelligent algorithm for expert and intelligent decision support system to help logistics companies to make decisions.

Using Clinical Expert Decision Support System BioAnalyst to Diagnose Colon Diseases

Using Clinical Expert Decision Support System BioAnalyst to Diagnose Colon Diseases

Expert strategic management systems in precision farming

Evaluating the modern market of the Internet of things, one should consider equipment connected into a single network, solutions, applications along the entire chain of product creation, including the end user. In this paper, such a binding is based on cloud information technologies. Through these technologies, the intellectualization of agrotechnology management is implemented through the creation of expert management decision support systems (DSS). The aim of the work is to consider the methodology for constructing DSS of strategic management in precision farming systems, where this type of management has not been implemented to date. To this kind of management, the task of forming strategies for the application of mineral fertilizers and ameliorants of prolonged action for all the years of various types of crop rotations is included. To solve the problem, an algorithm for the formation of optimal strategies for the application of mineral fertilizers and ameliorants, which is implemented in an analytical automated control system of agricultural technologies, through which a knowledge base (KB) is transmitted from the cloud system to local DSS, is substantiated. To select the best option from the knowledge base, the pattern recognition method is used. The technique was tested on arbitrary initial conditions of local systems, including extreme combinations of initial conditions. Based on the analysis of the loss of optimality associated with the discrepancy of the initial conditions on the local DSS and the KB, a method for controlling the formation of the BR, aimed at reducing these losses, is substantiated.

A robust expert decision support system for making real estate location decisions, a case of investor-developer-user organization in industry 4.0 era

PurposeThis paper aims to introduce a practical expert decision support system (EDSS) for performing location analysis and making real estate location decisions in the organization’s facility and real estate management (FREM) department in presence of several decision criteria, under risk and uncertainty. This tool is particularly useful for making strategic decisions in facility planning, portfolio management, investment appraisal, development project evaluations and deciding on usage possibilities in an unbiased, objective manner.Design/methodology/approachThe proposed EDSS uses fuzzy logic and uncertainty theory as two of the most useful tools to deal with uncertainties involved in the problem environment. The system performs an unbiased mathematical analysis on the input data provided by the decision-maker, using a combination of Analytical Hierarchy Process (AHP) and Global Criterion Method; determines a suitable compromise level between the objectives; and delivers a set of locations that complies best with the outlined desires of the management as the final solution. The application of the system is tested on a real case and has delivered satisfactory results.FindingsThe proposed EDSS took the defined objectives, the list of alternative locations, and their attributes as the required input for problem-solving, and used a combination of AHP, Possibilistic approach, and global criterion method to solve the problem. The delivered outcome was a set of proper locations with the right attributes to meet all objectives of the organization at a satisfactory level, confirmed by the problem owners.Originality/valueThe application of such a system with such a degree of preciseness and complexity has been very limited in the literature. The system designed in this study is an Industry 4.0 decision making tool. For designing this system several body of knowledge are used. The present study is particularly useful for making strategic decisions in the domains of portfolio management, investment appraisal, project development evaluations and deciding on property usage possibilities. The proposed EDSS takes the information provided by the experts in the field (through qualitative and quantitative data collecting) as the inputs and operates as an objective decision-making tool using several bodies of knowledge considering the trends and developments in the world of FREM. The strong scientific method used in the core of the proposed EDSS guarantees a highly accurate result.

Применение факторного моделирования для анализа социально-экономической безопасности страны

Ensuring the socio-economic security of the country is one of the main priorities of state policy. An im-portant means of its implementation is the devel-opment and implementation in government bodies of various levels of analytical and expert computer decision support systems. The paper considers the experience of the authors in the formation of a fac-tor model of socio-economic security at the federal level. The features of the application of factor mod-eling for the analysis of socio-economic security of the country are considered. The advantage of factor models for solving the problem of analyzing eco-nomic and social processes is shown. The concep-tual scheme of the factor model is proposed, the features of the selection and formalization of key groups of factors (aspects of the object, external and control effects, modeling results) are consid-ered. The scheme of the process of developing a scenario for countering threats to the socio-economic security of the country is proposed and the algorithms for its functioning are disclosed.

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification.

The European Commission’s recommendation on the definition of nanomaterial (2011/696/EU) established an applicable standard for material categorization. However, manufacturers face regulatory challenges during registration of their products. Reliable categorization is difficult and requires considerable expertise in existing measurement techniques (MTs). Additionally, organizational complexity is increased as different authorities’ registration processes require distinct reporting. The NanoDefine project tackled these obstacles by providing the NanoDefiner e-tool: A decision support expert system for nanomaterial identification in a regulatory context. It provides MT recommendations for categorization of specific materials using a tiered approach (screening/confirmatory), and was constructed with experts from academia and industry to be extensible, interoperable, and adaptable for forthcoming revisions of the nanomaterial definition. An implemented MT-driven material categorization scheme allows detailed description. Its guided workflow is suitable for a variety of user groups. Direct feedback and explanation enable transparent decisions. Expert knowledge is held in a knowledge base for representation of MT performance criteria and physicochemical particle type properties. Continuous revision ensured data quality and validity. Recommendations were validated by independent case studies on industry-relevant particulate materials. Besides supporting material identification and registration, the free and open-source e-tool may serve as template for other expert systems within the nanoscience domain.

Decision-making process modeling for occupational safety management system based on a fuzzy analysis of production incidents

The article proposes a decision-making model using a fuzzy analysis of production incidents for various factors. For the analysis, a database of factors of industrial incidents that occurred in the Belgorod region in 2007-2017 is used. The model is based on a quantitative assessment of the degree of belonging of the current value of the state parameters to each of the scenarios of severity of consequences at the corresponding point. This model is implemented in an expert decision support system for occupational safety and risk management. The use of such systems allows one to provide multivariate analysis of occupational injuries and occupational risk factors and to identify groups of workers for whom the level of occupational risk exceeds acceptable values.

Precision Oncology-The Quest for Evidence.

The molecular characterization of patient tumors provides a rational and highly promising approach for guiding oncologists in treatment decision-making. Notwithstanding, genomic medicine still remains in its infancy, with innovators and early adopters continuing to carry a significant portion of the clinical and financial risk. Numerous innovative precision oncology trials have emerged globally to address the associated need for evidence of clinical utility. These studies seek to capitalize on the power of predictive biomarkers and/or treatment decision support analytics, to expeditiously and cost-effectively demonstrate the positive impact of these technologies on drug resistance/response, patient survival, and/or quality of life. Here, we discuss the molecular foundations of these approaches and highlight the diversity of innovative trial strategies that are capitalizing on this emergent knowledge. We conclude that, as increasing volumes of clinico-molecular outcomes data become available, in future, we will begin to transition away from expert systems for treatment decision support (TDS), towards the power of AI-assisted TDS—an evolution that may truly revolutionize the nature and success of cancer patient care.

MODERN INFORMATION TECHNOLOGIES AS THE BASIS OF THE EDUCATIONAL INFORMATIZATION PROCES

The article is devoted to the analysis and research of innovative methods of teaching students in the conditions of the development of the information technology industry. Actual problems of professional training of specialists in the conditions of the development of the information society are analyzed and ways of their solution are investigated. Analyzed the role of information technology in higher education institutions. Currently, the main directions in the educational process are the use of automated systems and complexes, expert systems and decision support systems, the development of information technologies on the use of information technologies for modeling various objects and processes. The types of innovative teaching methods using modern technologies have been studied. At this stage they use powerful personal computers, new information and telecommunication technologies. The features of hardware and software for educational purposes are investigated. One of the special means of training is accessibility, the speed of obtaining knowledge at a convenient time, the possibility of an individual rate of knowledge acquisition. The directions of the use of information technology in the educational environment are substantiated.

Analysis of the behavior for operation parameters in the anaerobic digestion process with thermal pretreatment, using fuzzy logic

This article presents a study that identifies the variables with greatest impact on the biogas and methane production over a process with thermal pretreatment, to accelerate anaerobic digestion process in sewage sludge in a water treatment plant, for a poultry processing factory, by using fuzzy logic. The designed fuzzy logic model includes 688 inference rules, with a correlation of 99.3% between prediction data against experimental data, for the biogas variable; and 97% for the methane variable. The predictions of the fuzzy logic model were analyzed with response surface models, and it is concluded that the temperature and operating time variables are mutually determining in the biogas and methane production. Likewise, this research provides a methodology for the design of an expert decision support system that allows to evaluate and optimize a mesophilic anaerobic digestion process through a previous thermal treatment in order to improve the yields of biogas and methane in the treatment of effluent sludge from agroindustry. These results propose to diffuse logic as a reliable tool to make comparisons, and predictions for operation variables management on the treatment of residual sludge with thermal pretreatment on anaerobic digestion.

Knowledge Based Performance Evaluation and Predictive Model for Undergraduate Students

In educational data mining, the process of analysing and predicting from a pool of acquired data is a big challenge due to the influence of behavioural, environmental, parental, personal and social traits of students. While existing education predictive systems have used patterns generated from mined common factors to predict student performance based on subject, faculty, and grade amongst others, explicit traits, which defines a student are often neglected. Thus, such existing models are too general for specific and targeted analysis in more recent times when predictive features are although common but in real essence unique to individual students to a certain degree. Here, a Self-Academic Appraisal and Performance Predictive (SAAPP) system was developed to analyse and predict the overall performance of students before the expiration of their course duration. The inherent knowledge driven model analyses common available predictive internal and external factors, with probabilistic analysis of student academic history and pending courses. The system then builds a personal data centric system for individual student through a decision support expert system and a probabilistic optimal grade point analysis for more effective recommendation. The developed system is more accurate, reliable and precise in student performance classification with targeted recommendations.

Contestability and Professionals: From Explanations to Engagement with Algorithmic Systems

Algorithmic systems, particularly those based on machine learning, are increasingly being used to help us reason and make decisions. Effective systems that also align with societal values require not only designs that foster in-the-moment human engagement with such systems but also governance models that support ongoing critical engagement with system processes and outputs. Using the case of expert decision-support systems, we introduce the concept of contestability. We argue that contestability has distinct advantages over transparency and explainability, two policy objectives often offered as antidotes to the challenges posed by black-box algorithmic systems. We then discuss contestable design and governance principles as applied to clinical decision support (CDS) systems used by health-care professionals to aid medical decisions. We explain current governance frameworks around the use of these systems — particularly laws and professional standards — and point out their limitations. We argue that approaches focused on contestability better promote professionals’ continued, active engagement with algorithmic systems than current frameworks.

Expert Information-Analytical Decision Support System for Professional Risk Management Based on the Database of Real Cases of Industrial Injuries

The article shows that professional risk is caused by many interrelated factors, which is a big problem when developing ways to manage it. It is shown that one of the most effective professional risk management tools is the use of expert decision support systems using the methods of predictive modelling of events and incidents. The effective operation of such systems should be based on databases of occupational risk factors, created on the basis of a study of the causes of real cases of industrial injuries. In the work, analysis and statistical processing of data on 593 accidents for the period of time from 2012 to 2016 that occurred at the enterprises of the Belgorod region was carried out. Using univariate analysis of variance, the most significant factors affecting the level of industrial injuries were identified. A database on occupational injury factors was created, which became the basis for an expert decision support system in the field of occupational safety management.

Роль экономической информации в социально-экономическом развитии Азербайджана

Over the past decade, factors of socio-economic, scientific-technical and cultural development have emerged in Azerbaijan, which can be considered as important phases of the transition to an information society. The development concept “Azerbaijan 2020: A Look into the Future” envisages the further development of ICT and communication services, the creation of a reliable information security system and the further expansion of digital data transmission, as well as 100% implementation of e-government services. With full confidence it can be argued that in the middle of the XXI century. The leaders of the world economy and international trade will be countries with high technology and knowledge-intensive industries. This is facilitated by the use of new technologies of Expert Decision Support Systems. The main differences of Expert systems from other software products are the use of not only data, but also knowledge, as well as a special mechanism of solutions and new knowledge based on existing ones. As part of the reform being carried out in Azerbaijan and the intensive introduction of ICT in all spheres of state activity, several projects were implemented for the implementation of expert systems and innovation projects.

An expert decision support system for sandstone acidizing design

Based on the fundamentals of expert system (ES) and decision support system (DSS), we developed an integrated system, the expert decision support system (EDSS), to design and optimize sandstone acidizing. The new system combines knowledge of the ES with DSS models to facilitate decision-making for qualitatively and quantitatively acidizing sandstone reservoirs; this approach greatly strengthens the system’s working capability and widens its applicable range. This article introduces the design principle, system structure, functional modules, multi bases, and development process of the EDSS. We illustrate the representation method of the expert’s knowledge, establishing the knowledge decision tree, and creating quantitative mathematic models for decision support and inference process via different reasonings (rule-based vs. case-based). These methods and methodologies worked together to operate different functional modules for inference from both knowledge and calculation. The EDSS connected different design considerations of the acidizing technology. A field test case study proved that the proposal provided by the EDSS was very efficient.

Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression.

Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed.

Success history applied to expert system for underwater glider path planning using differential evolution

This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels.To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis.The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms existing literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision-makers, when they use the improved UGPP expert system yielding better trajectories.

Genetics and biology of prostate cancer.

Despite the high long-term survival in localized prostate cancer, metastatic prostate cancer remains largely incurable even after intensive multimodal therapy. The lethality of advanced disease is driven by the lack of therapeutic regimens capable of generating durable responses in the setting of extreme tumor heterogeneity on the genetic and cell biological levels. Here, we review available prostate cancer model systems, the prostate cancer genome atlas, cellular and functional heterogeneity in the tumor microenvironment, tumor-intrinsic and tumor-extrinsic mechanisms underlying therapeutic resistance, and technological advances focused on disease detection and management. These advances, along with an improved understanding of the adaptive responses to conventional cancer therapies, anti-androgen therapy, and immunotherapy, are catalyzing development of more effective therapeutic strategies for advanced disease. In particular, knowledge of the heterotypic interactions between and coevolution of cancer and host cells in the tumor microenvironment has illuminated novel therapeutic combinations with a strong potential for more durable therapeutic responses and eventual cures for advanced disease. Improved disease management will also benefit from artificial intelligence-based expert decision support systems for proper standard of care, prognostic determinant biomarkers to minimize overtreatment of localized disease, and new standards of care accelerated by next-generation adaptive clinical trials.