Information Decision Support Systems Research Articles

Study and Development of IOT based Smart Irrigation System using Soil Moisture and Weather Prediction

Abstract: The smart irrigation system was developed to optimize water use of crops. The system has a distributed wireless network of soil-moisture temperature sensors placed in the root zone of the plants. Also, a gateway unit handles sensor information, triggers, actuators, and transmits data to the web application. The intelligence of The proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web- services and a web-based information visualization and decision support system provides real-time information insights based on the analysis of sensor dataand weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging

Hierarchical Structure Objects Information Systems Decision Support Developing Process

Application of methods for designing decision support information systems for hierarchical systems is considered. Determining the interaction parameters of methods for designing decision support information systems for managing hierarchical production and technical systems is an urgent scientific problem, the which solution makes a significant contribution to the development of technologies for designing decision support systems. The process of operation of a decision support system for the analysis of hierarchies using the full graphical interface technology, a functional model of an expert information system for the analysis of hierarchical structures is presented. Hierarchical production and technical systems are complex, which entails great complexity in the development of decision support information systems to manage them. This complexity creates the problem of 'crisis' in the development of such systems for hierarchical production and technical systems. Despite many advantages and a long history of successfully applying the unified design method to the development of information systems, it does not completely overcome the problem of this crisis. To overcome this problem for hierarchical production and technical systems, it is proposed to modify the unified design method and use the synergistic effect from the combined use of structural, object and visual modelling methods in the form of a new design method - a modified system design process. The article presents the scheme and algorithm of the proposed method, the results of the analysis of its application. The possibility of its application to systems with a hierarchical structure is established. Unlike traditional models of information systems, the decision support information system model built according to the new method successfully integrates various subject areas of knowledge due to its visibility and support for object-oriented design technologies, which allows it to be effectively used to create an expert information system model for complex technical systems.

A trajectory data warehouse solution for workforce management decision-making

In modern workforce management, the demand for new ways to maximize worker satisfaction, productivity, and security levels is endless. Workforce movement data such as those source data from an access control system can support this ongoing process with subsequent analysis. In this study, a solution to attaining this goal is proposed, based on the design and implementation of a data mart as part of a dimensional trajectory data warehouse (TDW) that acts as a repository for the management of movement data. A novel methodological approach is proposed for modeling multiple spatial and temporal dimensions in a logical model. The case study presented in this paper for modeling and analyzing workforce movement data is to support human resource management decision-making and the following discussion provides a representative example of the contribution of a TDW in the process of information management and decision support systems. The entire process of exporting, cleaning, consolidating, and transforming data is implemented to achieve an appropriate format for final import. Structured query language (SQL) queries demonstrate the convenience of dimensional design for data analysis, and valuable information can be extracted from the movements of employees on company premises to manage the workforce efficiently and effectively. Visual analytics through data visualization support the analysis and facilitate decision-making and business intelligence.

Smart Irrigation and Crop Health Prediction

The scarcity of clean water resources around the globe has generated a need for their optimum utilization Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing are bridging the gaps between the cyber and physical world. IoT based smart irrigation systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture soil temperature, and environmental conditions along with the weather forecast data from the Internet. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information-insights based on the analysis of sensors data and weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.

Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods

When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.

ARTIFICIAL NEURAL NETWORK TRAINING BASED ON PERFORMANCE AND RISKS ASSESSMENT DATA OF THE INVESTMENT IN DIGITAL ASSETS

The problem of analyzing the results of training artificial neural networks based on data about the efficiency and risks of investing in digital assets, particularly in the context of managing the buying and selling process of cryptocurrencies, has been investigated. The approach for solving this problem is based on the application of game theory as the main principle for forming the architecture of the artificial neural network. Combining two fundamental theories - game theory and neural networks - allows the creation of intuitively understandable and effective intelligent information systems for decision support in various application areas, such as finance, economics, and resource management. Special attention is paid to considering fuzzy parameters and uncertainties in market conditions, reflecting the real circumstances of investing in cryptocurrencies and other digital assets. The article proposes a series of methods for training and adapting the artificial neural network within the developed approach, as well as recommendations for evaluating its effectiveness and stability. The possible areas of application and prospects for further development of this methodology in the context of the digital asset market have been analyzed. The application of the developed methodology for analyzing the results of artificial neural network training has been illustrated, and its high efficiency in predicting investment performance and risks in digital assets has been confirmed. The issues and limitations that may arise during the use of this methodology were highlighted, and possible ways to overcome and improve them have been proposed..

Ферма данных: информационная система сбора, хранения и обработки неструктурированных данных из разнородных источников

The original information system «data farm» is presented. Today, the successful application of artificial intelligence algorithms, primarily deep learning based on artificial neural networks, almost completely depends on the availability of data. And the larger the amount of these data (big data), the better are the results of the algorithms execution. There are well-known examples of such algorithms from Facebook, Google, Microsoft, Yandex, etc. The data must contain both the training sample and the test one. Moreover, the data must be of good quality and have a certain structure, ideally, be labeled in order for the learning algorithms to work adequately. This is a serious problem requiring huge computational and human resources. This paper is dedicated to solve this problem. Today data farm is a rather complex information system built on a modular basis, similar to the well-known Lego constructor. Separate modules of the system are various modern algorithms, technologies and entire libraries of artificial intelligence, and all together they are designed to automate the process of obtaining and structuring high-quality big data in various subject domains. The system has been tested on data of COVID-19 in regions of Russia and countries around the world. In addition, a user-friendly interface for visualizing collected and processed on the farm data was developed. This makes it possible to conduct visual numerical experiments of computer simulation and compare them with real data, turning the farm into an intelligent decision support information system.

Creation of intelligent information decision support systems

The use of intelligent information decision support systems implies considering the problem area's specifics. The object of study is characterized by the following set of features: - quality and efficiency of decision-making; - vagueness of goals and institutional boundaries; - the plurality of subjects involved in solving the problem; - randomness; - a plurality of mutually influencing factors; - weak formalizability, uniqueness of situations; - latency, concealment, the implicitness of information. For the efficient and reliable functioning of agricultural facilities and enterprises, it is necessary to create and implement intelligent information systems. Over the past quarter of a century, domestic information systems have undergone a progressive evolution, both in terms of developing the theoretical principles of their construction and implementing these systems. The restructuring of agriculture, the market conditions for the functioning of objects, and agriculture enterprises have their characteristics and problems. Building the structure of intelligent decision support information systems is primarily associated with building a system model, in which both traditional elements of the control system and knowledge processing models should be defined. To solve these problems, methods of system analysis were used. The key research method is the optimization of data representation structures of databases and knowledge. The following relational data representation structures have been identified: relations, attributes, and values. In the relational model, structures are not specially allocated to represent data about entity relationships. Semantic networks use a three-level representation of data on entities and a four-level representation of data on entity relationships. The conducted studies have shown that in data representation structures, entity-relationship models are a generalization and development of the structures of all traditional data models since only in this data model there are 4-level data representations of both entities and relationships. All other traditional models are some special cases of the most general entity-relationship model.

Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture

Precision Agriculture (PA) is an approach to maximizing crop productivity in a sustainable manner. PA requires up-to-date, accurate and georeferenced information on crops, which can be collected from different sensors from ground, aerial or satellite platforms. The use of optical and thermal sensors from Unmanned Aerial Vehicle (UAV) platform is an emerging solution for mapping and monitoring in PA, yet many technological challenges are still open. This technical note discusses the choice of UAV type and its scientific payload for surveying a sample area of 5 hectares, as well as the procedures for replicating the study on a larger scale. This case study is an ideal opportunity to test the best practices to combine the requirements of PA surveys with the limitations imposed by local UAV regulations. In the field area, to follow crop development at various stages, nine flights over a period of four months were planned and executed. The usage of ground control points for optimal georeferencing and accurate alignment of maps created by multi-temporal processing is analyzed. Output maps are produced in both visible and thermal bands, after appropriate strip alignment, mosaicking, sensor calibration, and processing with Structure from Motion techniques. The discussion of strategies, checklists, workflow, and processing is backed by data from more than 5000 optical and radiometric thermal images taken during five hours of flight time in nine flights throughout the crop season. The geomatics challenges of a georeferenced survey for PA using UAVs are the key focus of this technical note. Accurate maps derived from these multi-temporal and multi-sensor surveys feed Geographic Information Systems (GIS) and Decision Support Systems (DSS) to benefit PA in a multidisciplinary approach.

Models and methods for assessing the security of an informatization object

The paper substantiates the need of creation an information decision support system in the development of systems for protecting informatization objects. Analyzes of existing systems in various fields of activity, the requirements for the functionality of the system in relation to the field of information protection, methods for developing models of functioning of protected information systems in a destructive environment impact on the basis of Bayesian networks are considered. The paper gives a description of a typical module functioning of this model. The structures of probabilistic models of the relationship of vulnerabilities, information security threats, methods and scenarios for their implementation, the formation of measures to protect informatization objects, the formation and assessment of the risks of incidents and their damage are considered. Clusters of typical information security events, methodological apparatus for calculating the joint distribution of the probabilities of protective and destructive events are determined. Finally, typical chains of interconnections of such events are identified. Mathematical apparatus for calculating their probabilities, a verbal description of the patterns of their mutual influence, and a method for converting quantitative probabilistic values of informatization object security indicators into qualitative ones are presented, and the results of the study are summarized.

DECISION SUPPORT SYSTEM OF REWARDING ON LECTURER PERFORMANCE USING FUZZY TSUKAMOTO METHOD CASE STUDY AT MATARAM UNIVERSITY OF TECHNOLOGY

To prepare quality and character human resources, Mataram Technological University strives to provide the best in carrying out the tridharma activities of higher education, one of which is by giving rewards in the hope that morale and loyalty can continue to be improved. However, the gift-giving system that the Mataram Technological University has implemented has not been able to bring about change because the gift-giving system is incorrect. This is because the applied reward-giving assessment system only refers to the assessment without paying attention to other criteria in the tridharma of higher education. Such as the implementation of learning, Research, and community service. Therefore, to overcome this problem, a decision support information system for awarding lecturer performance is needed, which is built using the fuzzy Tsukamoto method by considering several criteria such as Presence, Research Results, and Community Service Results. Lecturer Performance Index in carrying out the learning process. With this decision support system, the implementation of the Tridharma carried out by lecturers can continue to monitor the system and improve the quality and accreditation of study programs and universities.

Cloud Mining Actionable Pattern Discovery In Big Data : A Survey

Everyday several tons of data are generated by Social Media and Education sectors. Mining this data can provide a lot of meaningful insights on how to improve user experience in social media and how to improve student learning experience. Action Rule Mining is a method that can extract such actionable patterns from large datasets. Action Rules provide actionable suggestions on how to change the state of an object from an existing state to a desired state for the benefit of the user. There are two major frameworks in the literature of Action Rule mining namely Rule-Based method where the extraction of Action Rules is dependent on the pre-processing step of classification rule discovery and Object-Based method where it extracts the Action Rules directly from the database without the use of classification rules. Hybrid Action rule mining approach combines both these frameworks and generates complete set of Action Rules. The hybrid approach shows significant improvement in terms computational performance over the Rule-Based and ObjectBased approach. This paper gives a brief survey of previous works on action rule mining algorithms, and discusses Action Rule Mining on BigData and in a distributed environment. The applications of actionable action rules in BigData include business opinion mining, educational opinion mining, medical data decision support systems, text emotion detection, and social media data recommendations.

Model of knowledge representation in the system of information support for decision making

Objectives. The problem of mathematical modeling of the knowledge in the information support system for decision-making is being solved. The approach is based on the formalization of semantic relations in the language. The formalization of such relations provides an adequate representation of knowledge and serves as a theoretical basis for the algorithmization of user interaction with the system.When solving the problem of knowledge modeling, three main goals are pursued: the construction of a mathematical model of semantic relations, the study of the properties of marginal syntagmas and the construction of algorithms for the formation of a dictionary of syntagmatic structures.Methods. Methods of set theory, graph theory and mathematical linguistics are used.Results. A mathematical model of knowledge representation in the information decision support system has been developed. The concepts of semantic relations in the language are formalized and the properties of marginal syntagmas are investigated. The proven properties of such structures provide the algorithmization of information processes in the system under consideration.Conclusion. As an implementation of the model proposed in the article, algorithms for creating dictionaries of syntagmas and syntagmatic structures have been developed. When forming a set of syntagmas, the assertions about proved in the article marginal syntagmas are used. A set of syntagmatic structures is built taking into account their typology. Syntagmatic structures in the dictionary acquire the status of communicative fragment, i.e. stable phrases to synthesize the information system language.

Automatic irrigation control system using Internet of Things(IoT)

The water deficiency for agriculture around India has pushed a need for optimal utilization of it. Internet of Things (IoT) is today’s emerging trend which bridges the gap of real world with the technology. An IoT based automatic irrigation control systems helps in the optimal water utilization for the crop fields. This article proposes an automatic irrigation control system which is based on Internet of Things to predict water requirement of a crop field by processing the wireless sensor network’s (WSN) sensor data like relative humidity, moisture of soil, temperature of soil, and radiation of light (Ultraviolet) along with the Indian Meteorological Department’s weather forecast. Considering the sensed data from the environment with the weather forecasting data, a machine learning algorithm for soil moisture prediction is developed to evaluate the water requirement for a particular area of agricultural land for that crop yield. Real time information of the agriculture landscape is provided through a smart information processing and decision support system which is implemented using schematic web service. This article discusses and describes the system implementation in detail. The results by the automatic irrigation control system based on the soil moisture prediction algorithm are presented. The irrigation control system is completely automated and soil moisture prediction results are nearest to reality.

Managing outpatient flow via an artificial intelligence enabled solution

AbstractAn intelligent decision support information system is applied to develop an artificial intelligence (AI)‐driven solution to reduce patient waiting time in a hospital without an appointment system to improve healthcare service quality. The bottlenecks were identified, and then the healthcare service process was reengineered through the proposed shortest‐consultation time (SCT) model to increase the capacity for serving patients and the synchronization between capacity and patient arrival patterns. The study made several meaningful observations: (1) It is feasible to apply an AI‐driven solution to reduce patient waiting time in a hospital with no appointment system to improve healthcare service quality; (2) analytical models are helpful in identifying characteristics of patient flow problems; (3) the implementation of a few performance factors gives most of the improvements, and (4) theory of constraint (TOC) is a method that can be applied to improve patient service qualification.

Sistem Penunjang Keputusan Untuk Rekomendasi Pemilihan Jurusan di SMK Yasbam Menggunakan Metode Simple Additive Weighting

The process of processing grades and determiningrecommendations for SMK majors is still generally done manually,while the results of the decisions must be announced on the sameday. The selection of majors at Yasbam Vocational School iscurrently through an academic test that is seen from the criteria forthe average value of report cards, TKJ theory tests, OTKP theorytests, TBSM theory tests, and specialization questionnaires.Therefore, a decision support system (SPK) is needed that can helpto solve the problem of selecting majors according to the abilities ofprospective students. This majors selection system uses the simpleadditive weighting (SAW) method for better decision making. Theresearch objectives of the decision support system for the selectionof majors at SMK Yasbam are to obtain the results of the analysisusing the SAW method, to obtain the results of the designinformation system to support the decision support for the selectionof majors at SMK Yasbam and to obtain a decision supportinformation system to recommend the selection of majors. Themethod used in this research is Research and Development. Theresults show that this system is divided by users, namely Admin andStudents. In the implementation of the Admin interface system, thereare eight menus, namely Login, dashboard, criteria data, criteriaweights, alternative data, alternative values, selection results,recommendation results, majors and users. While in the studentdesign there is an interface design, namely Login, dashboard,recommendation results. The design of this interface is based on thedesign on context diagrams, data flow diagrams and entityrelationship diagrams that have been created by applying them tothe form. Based on the test results, it was concluded that studentshad received a major recommendation from the decision supportsystem for selecting majors at SMK Yasbam using the SAW (SimpleAdditive Weighting) method.

Information and analytical system of decision-making support for agricultural enterprises of Northern Kazakhstan

This article presents a description of the developed information and analytical decision support system for choosing the optimal production scenario for an agricultural enterprise IAS PPR Kazagro Choice. The problem of making managerial decisions on scenarios for the production of agricultural enterprises in Northern Kazakhstan is due to the presence of many criteria (parameters) of production and market conditions. The correctness of the consideration of the criteria predetermines the quality of management decision-making according to the preferred scenario for the functioning of an agricultural facility. The IAS PPR Kazagro Choice contains the concept of finding the best (optimal) production scenario among all possible alternative scenarios. The development of IAS PPR Kazagro Choice is based on the author’s mathematical model with a dominant hierarchy of the problem of choosing a production scenario and on the implementation of the hierarchy analysis method. IAS PPR Kazagro Choice makes it possible for a decision-maker to choose a preferred (optimal) scenario for the production of an agricultural enterprise in conditions of multifactorial influence and the availability of expert information. Distinctive characteristics of IAS PPR Kazagro Choice: focus on solving poorly structured problems; combination of traditional methods of access and processing of computer data with the capabilities of mathematical modeling. The specific applied value of the work lies in the possibility of using an adaptive implementation model for making managerial decisions on the choice of the preferred production scenario for a wide range of agricultural enterprises.

A Decision Support System for Marine Regulation

The implementation of marine spatial data management and decision support based on GIS provides a powerful tool for marine regulation. This study constructed a three-dimensional spatiotemporal data model of marine resources that is hierarchical, partitioned, and classified according to the management principle of using an underground resource layer, surface matrix layer, surface overburden layer, and management layer, taking the marine resource entity as the unit. Key research topics include marine multi-source information integration, visual expression, and spatiotemporal topology analysis for typical application scenarios such as sea area use, island development and utilization, and marine environmental monitoring and protection. The study developed a marine information decision support system with comprehensive integration and visual analysis of information on maritime islands, early warning and monitoring, the marine economy, and the marine environment. The system was applied to dynamic regulation of sea area use, spatiotemporal characteristic analysis of marine resources, the regulation of human activities in marine protected areas, and the monitoring of sea level rise. Practice has proved that a three-dimensional spatiotemporal data model of marine resources can meet the application requirements of “One Map”, the basic land and spatial information platform.

Agent-oriented data clustering for medical monitoring

Medical data processing is one of the priority machine learning areas. Usually, data obtained in the process of medical patient monitoring are complex and have a different nature. Solving the problem of clustering, classification, or forecasting problem these data requires the creation of new methods or improvement of existing methods to improve the decision accuracy and effectiveness. The classical clustering approaches and the c-means fuzzy clustering method were analyzed. Based on the multiagent systems theory, it is proposed to use in the c-means method the separate rules for selecting elites when forming clusters and selecting the best of them in accordance with the chosen intra-cluster distance measures. The result of solving such a problem is the number of clusters, as well as the number of elements in them. The method quality was tested on Fisher iris data set using three measures of intra-cluster distance: Mahalanobis distance, Mahalanobis distance considering the membership function, and Kullbak-Leibler entropy. The highest accuracy of 98% was obtained for the distance measured by the Kullbak-Leibler entropy. Therefore, this measure was chosen to solve the clustering problem of medical monitoring data for prostate disease. Medical monitoring data were divided into four classes of patient states: “healthy persons”, “non-metastatic patients”, “metastatic patients” and “hormone-resistant patients”. The accuracy of clustering according to medical data was 95,6%. In addition to accuracy, the confusion matrix, ROC- and LF-curves were used to assess the method quality. The minimum value of the ROC-curve was 0.96 for Fisher's irises and 0.95 for medical monitoring data, which characterizes the high quality of the proposed clustering method. The loss function value is also quite small (-0.056 and -0.0176 for each considered data set), which means that the optimal cluster number and the distribution of data over them are obtained. Based on the obtained results analysis, the proposed method can be recommended for use in medical information and diagnostic decision support systems for clustering monitoring data.

Information decision-making support for periodic maintenance of gas balloon equipment

The issues of information support for decision-making during periodic maintenance of gas-cylinder equipment of vehicles running on natural gas, ensuring safe operation, contributing to an increase in the efficiency of transportation of goods and goods: reducing vehicle downtime due to failure of the fuel system using gas fuel by 10–18%; reducing the likelihood of functioning of gas equipment in hazardous modes by 12–17%. The technical requirements for gas equipment and procedures for its periodic maintenance are formulated. The concept of the formation and application of a unified state register of gas equipment, which is a tool for limiting admission to the turnover of products that does not meet regulatory and market requirements, is proposed. An information-logical model of decision-making support has been developed, which allows, depending on the current technical condition of assemblies and parts, based on the specified requirements for the duration of the period between maintenance, to propose the best option for carrying out diagnostic, repair and commissioning measures for gas-cylinder equipment of vehicles. The procedure for the formation of an extended list of measures for periodic maintenance of gas equipment is presented on the example of individual nodes of the fuel system of a vehicle running on compressed natural gas. The software implementation of the information- logical model is carried out in the form of a plug-in extension to the database core of the decision support information system for the operation of gas equipment, implemented on the Oracle database management system with integrated support for the high-level object- oriented language Java. For the convenience of working with the information-logical model, a computational and analytical module of the information system for decision-making support during the operation of gas-cylinder equipment with elements of fuzzy logic has been developed. The model was tested on the example of the implementation of measures during the periodic maintenance of the gas equipment of the KamAZ 65116 truck.