Smart Decision Support System Research Articles

Smart decision support system for keratoconus severity staging using corneal curvature and thinnest pachymetry indices

BackgroundThis study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements.MethodsA clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience.ResultsThe performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality.ConclusionThe developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.

Sistem Pendukung Keputusan Cerdas untuk Pemilihan Jenis Tanaman Pertanian Kota

Urban farming activities have become increasingly popular to meet their food needs in urban areas. Jakarta as provinces with a population density of high, have a program of urban farming is developed by the farmers to urban Balkot Farm. This study aims to support system to develop a clever move the crop farm a town with the simple additive weighting (SAW). The methods used to obtain the highest related alternative plant assessed according to its parameters that affect the eligibility of land and a room with a variety of plants. The methodology used software development life cycle (SDLC) prototyping model consisting of five of the communication, quick plan, modeling quick design, construction of prototype deployment and delivery and feedback. Data collection method using interviews and the study of literature. Research results of a web application system that has an alternative menu, criteria, subcriteria, rating match and results. Smart decision support system based on black box testing and user acceptance testing successfully shows menus according to land criteria that will be used according to stakeholder needs.

Farming in the Digital Age: Smart Agriculture with AI and IoT

This review article titled “Revolutionizing Agriculture: The Impact of AI and IoT” delves into the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It encapsulates the burgeoning wave of digitalization and its implications for data-driven farming practices. The paper comprehensively reviews the advancements in digital agriculture management, highlighting the integration of AI, IoT devices, machine learning, and sensor technology in enhancing farming efficiency and productivity. Emphasis is placed on the use of robotics, drones for crop monitoring, and smart irrigation systems, which leverage real-time data for optimized agricultural operations. The role of high-speed 5G networks in enabling these smart farming solutions is also discussed, particularly their impact on data transmission and connectivity in rural settings. Challenges and opportunities in implementing Smart Decision Support Systems (SDSS) in developing countries are explored, focusing on real-time soil analysis and decision-making aids for farmers. The review further discusses a proposed cloud-based system for remote farm monitoring, employing AI-based models for efficient data processing and improving farming practices. This article aims to provide a nuanced understanding of the current and future potential of AI and IoT in reshaping agricultural practices, addressing global food security challenges, and supporting sustainable development.

Urban community governance and machine learning: practice and prospects for intelligent decision making

Abstract Urban community governance faces unprecedented challenges, but machine learning provides new ideas for it. The objective of this study is to examine the use of machine learning technology in urban community governance to enhance governance efficiency and decision-making quality. The study constructs an artificial neural network intelligent decision support model based on genetic algorithm optimization, which is based on the operational requirements of a smart decision support system. A weighted fuzzy inference network is further developed by the fusion of fuzzy logic and neural networks to enhance the system’s ability to deal with uncertainty and ambiguity. Next, prediction tests were conducted on the development levels of six communities to validate the model’s effectiveness. After 1686 training steps, the error squared SSE drops below 0.2%, according to the results. The composite index’s prediction error was 5.12%, while the minimum error was −1.79%. The predicted rankings of the communities did not change from the actual ones, which was in line with the normal trend, and the algorithmic model achieved better prediction results. This study not only provides a new intelligent decision support tool for community governance but also sets a theoretical and practical foundation for the intelligent development of urban community governance in the future.

Research on intelligent legal decision support system based on big data analysis

Abstract This paper constructs an intelligent decision support system using text combination feature extraction, case similarity, XBoost sentencing prediction model, and other related technologies. The performance of the smart decision support system is tested for grabbing responses and monitoring response reliability. The XGBoost algorithm is used to construct the sentencing prediction model to achieve the intelligence of sentencing prediction. The effectiveness of the sentencing prediction model is examined by comparing the XGBoost model with different algorithms (Random Forest, CBDT, CNN). The practical application of intelligent decision support systems was summarized, highlighting the positive and negative effects. The results show that the overall responsiveness performance of the intelligent decision support system’s magisterial document capture and retrieval is reliable, and the number of captured and retrieved magisterial documents rises as the capture time increases. In XGBoost’s sentencing prediction, nine overlapping parts of the ten keywords were extracted for sentence and fine. In contrast, the keyword similarities are all higher than 0.5, and the difference between the predicted and real sentence values and fine is small. The intelligent decision support system has resulted in a gradual decrease in the number of court cases and an improvement in efficiency. The re-sentencing and retrial initiation rate decreased by over 20% from 2019 to 2023.

An Information Service Platform for Decision Support in Academic Admissions Using Data Fabrics and Artificial Intelligence

This paper presents the architecture of an information service platform for decision support in academic admissions, utilizing data fabrics and artificial intelligence. The factors affecting students’ further education can be classified into four main types: (1) the course, (2) image, (3) personal reasoning of the student, and (4) public relations. The process of providing information for decision support in academic admissions can be divided into six stages: (1) collecting information, (2) matching study guidance, (3) recommending appropriate education, (4) confirming information, (5) assessing student admissions, and (6) providing feedback. Data fabric is an increasingly popular technology application for data management. The data fabric architecture consists of six layers: (1) an augmented data catalog, (2) a knowledge graph enriched with semantics, (3) metadata activation, (4) a recommendation engine for active metadata, (5) data preparation and integration, and (6) orchestration and data operations. A smart decision support system (DSS) technology is used to assist in decision-making. The results showed that this architecture has an excellent level of suitability (mean = 4.56, standard deviation = 0.35). It can be applied to a university to help it become a digital university and align with its mission.

Internet of Things (IoT) in digital agriculture: An overview

AbstractClimate change, land degradation, and limited land and water resources have challenged our ability to meet the food demand of a rapidly growing population. To tackle this challenge, modern agricultural systems are relying on new technologies like the Internet of Things (IoT) to improve agricultural productivity and resource use efficiency. Although IoT has gained considerable attention in the last few years, the key concepts of IoT and their applicability across different domains of agriculture are still new to many researchers, practitioners, managers, and policymakers. In this paper, we provide a comprehensive review of the use of different IoT platforms, wireless sensor networks, and other associated technologies like remote sensing, cloud computing, and big data analytics in digital agriculture. The review also explores the use of communication technologies, microcontrollers, and machine learning in smart irrigation and decision support systems. The necessity of interoperability (data transfer and communication without human interference) among devices is discussed in detail with regard to facilitating and exchanging agricultural data more effectively. The discussion also includes opportunities and challenges in standardizing IoT; developing energy‐efficient and affordable technologies; and improving data collection, transfer, storage, processing, security, anonymity, and privacy. This paper further calls for collaborative research in sensor and communication technologies in the agricultural sector.

A smart Sustainable decision Support system for water management oF power plants in water stress regions

Power Plants (PPs) is considered as critical facilities in each region because of essential role through energy generation processes. These facilities are also depended to water availability especially in water stress areas. Due to the critical water shortage in many areas around the world, it is necessary to make an optimal condition among water consumption and the increasing demand for electricity to prevent any further conflict of interests between industry, householders and the environmental goals. There are different techniques for controlling Water Consumption (WC) in these industries. This paper develops a smart Decision Support System (DSS) for monitoring, prediction and control sections based on Artificial Intelligent (AI) and integration of the PESTEL matrix and Multi Criteria Decision Making (MCDM) methods. Monitoring section comprises Fuel Consumption (FC), Atmospheric Temperature (AT), Power Plant Temperature (PPT) and Power Plant Efficiency (PPE), in which FC has the most influence on WC based on ANOVA evaluations in both cold and warm seasons. The prediction results have illustrated that Adaptive Neuro Fuzzy Inference System model is more efficient for the WC estimation with a correlation coefficient over 0.99. Ordered Weighted Averaging (OWA) also demonstrated that in the optimistic and pessimistic states, the most priority is linked to E3 (Establishment of evaporation control systems by contractor companies and concluding a guaranteed purchase contract with a power plant worth one and a half times the current amount of water price). In the last step of technical approaches, the smart controlling system is added for execution of water-energy nexus in the PP based on proportional–integral–derivative controller system. Finally, the performance of the DSS is approved with more than 80% agreement of experts and more than 90% precision in prediction procedure through this investigation. Application of this DSS can also be helpful for developing countries to achieve the UN Sustainable Development Goals.

An evaluation of blood components inventory management during Hajj and Umrah season at Makkah hospitals

Objectives: The current study aims to utilize blood bank data collected from the Army forces hospital in Makkah to improve transfusion services at Makkah hospitals and evaluate the inventory system during the Hajj season.
 Methods: Three years' results showed that blood centre production is not constant, with an average of 1800 units during Hajj season that includes Cryo (450), FFP (450), PLT (450), and PRBC (450) units. Moreover, data indicated that the average usage of units is 1119 during the same season (120 Cryo, 424 FFP, 207 PLT and 368 PRBC).
 Results: This demonstrated that the inventory of blood units fluctuates during the year with an overproduction of Cryo (80%), FFP (67%), PLT (83%) and PRBC (57%) units in Hajj Season. It can be concluded that the manner and effectiveness with which the present issues in Makkah blood banking are having a major impact on the current system used to obtain the nation's blood supply and the safety, adequacy, and operational cost of that supply.
 Conclusion: The current study recommends building a generic data warehouse with smart decision support systems that use artificial intelligence to maintain blood bank production and inventory system during the year, especially during Hajj and Umrah seasons.

Smart Decision-Support System for Pig Farming

There are multiple participants, such as farmers, wholesalers, retailers, financial institutions, etc., involved in the modern food production process. All of these participants and stakeholders have a shared goal, which is to gather information on the food production process so that they can make appropriate decisions to increase productivity and reduce risks. However, real-time data collection and analysis continue to be difficult tasks, particularly in developing nations, where agriculture is the primary source of income for the majority of the population. In this paper, we present a smart decision-support system for pig farming. Specifically, we first adopt rail-based unmanned vehicles to capture pigsty images. We then conduct image stitching to avoid double-counting pigs so that we can use image segmentation method to give precise masks for each pig. Based on the segmentation masks, the pig weights can be estimated, and data can be integrated in our developed mobile app. The proposed system enables the above participants and stakeholders to have real-time data and intelligent analysis reports to help their decision-making.

A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model

Automatic screening approaches can help diagnose Cardiovascular Disease (CVD) early, which is the leading source of mortality worldwide. Electrocardiogram (ECG/EKG)-based methods are frequently utilized to detect CVDs since they are a reliable and non-invasive tool. Due to this, Smart Cardiovascular Disease Detection System (SCDDS) has been offered in this manuscript to detect heart disease in advance. A wearable device embedded with electrodes and Internet of Things (IoT) sensors is utilized to record the EKG signals. Bluetooth is used to send EKG signals to the smartphone. The smartphone transfers the signals through an Android app to a pre-trained deep learning-based architecture deployed on the cloud. The architecture analyzes the EKG signal, and a heart report is communicated to the patient and advises further preventive action. We offered an ensembled Convolution Neural Network architecture (ConvNet) and Convolution Neural Network architecture - Long Short-Term Memory Networks (ConvNet-LSTM) architecture to detect atrial fibrillation heartbeats automatically. The architecture utilizes a convolutional neural network and long short-term memory network to extract local correlation features and capture the front-to-back dependencies of EKG sequence data. MIT-BIH atrial fibrillation database was utilized to design the architecture and achieved an overall categorization accuracy of 98% for the test set's heartbeat data. The findings of this work show that the suggested system has achieved significant accuracy with the ensembling of models. Such models can be deployed in wearable devices and smartphones for continuous monitoring and reporting of the heart.

Integration of industrial IoT architectures for dynamic scheduling

Technology advancement in smart sensors, cloud computing, and decision support systems is pressuring the industry to adopt more flexible data-based solutions. One of the areas that can benefit from this progress is dynamic scheduling, which can improve production efficiency by tracking resource availability, job changes, and user commands. Challenges include setting triggers through the simultaneous integration of information from cloud-based enterprise databases and operational sources in the Industrial Internet of Things (IIoT). This paper proposes an integrated Information Technology - Operational Technology solution to support dynamic scheduling and rescheduling operations in a personalized production environment. It presents an implementation to a real-world application: an analytical quality control laboratory in the pharmaceutical industry. The resulting integration of intelligent sensors and business events in a fog computing architecture allows the generation of rescheduling triggers to specific online events. This paper focus on the definition of the online events affecting the operations and the cloud-fog-edge IIoT architecture used to support the implementation, and on the rescheduling triggers. The optimized reschedule of the use case shows that moving the computation closer to the cloud improves the CPU run time for larger instances. However, the combined CPU run time with the data exchange and querying introduces a non-negligible communication delay for smaller instances. In situations where fast scheduling solutions are required, fog computing near the edge is the best approach. On the other hand, for larger-size instances, moving the computation closer to the cloud is the recommended approach.

To promote experience sharing in urban transitions: An integrated framework of experience mining and triangulation

Many cities have put relentless efforts in promoting transformational changes to tackle urban challenges, accumulating considerable experiences and knowledge for other cities to learn. To reuse the successful experiences in an effective way, previous studies have developed experience mining systems, which adopt Case Base Reasoning as smart decision support systems to provide valuable references on how to manage and solve the current problems. However, the experience mining systems generally pay limited attention to the feasibility and applicability of transition experiences for the target city, making it challenging to validate results considering the complexity of urban transitions. To fill the gap, this paper proposes a methodological framework to evaluate matching degrees of past experiences by integrating experience mining with triangulation, aiming at selecting the most appropriate case for the target city from multiple perspectives. A case study of Beijing is further conducted, with different methodologies leading to distinguished results, which proves the necessity of employing various methods to enhance the validity of experience mining for urban transitions. It is envisaged that the integrated framework could help future practitioners and researchers to make better-informed choices with experience mining systems during experience learning or theory development in urban transitions.

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.

КОМПОНЕНТЫ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ УПРАВЛЕНИЯ МЕТРОЛОГИЧЕСКИМ ОБЕСПЕЧЕНИЕМ ПРЕДПРИЯТИЯ

Described the components of a distributed smart decision support system developed by the authors to manage the metrological support of the enterprise and processes related to metrological control in production

Smart Pre-Seeding Decision Support System for Agriculture

In recent years, the Internet of Things (IoT) brings a new dimension for establishing a precision network connectivity of sensors, especially in the agriculture and farming industry, medical, economic, and several sectors of modern society. Agriculture is an important area for the sustainability of mankind engulfing manufacturing, security, and resource management. Due to the exponential diminishing of the resources, innovative techniques to support the subsistence of agriculture and farming. IoT aims to extend the use of internet technology to a large number of distributed and connected devices by representing standard and interoperable communication protocols. This paper brings up a solution by IoT, presents the design and implementation of a smart pre-seeding decision support system for agricultural modernization. This project is accomplished by understanding the real-time circumstances in the agriculture field using wireless technology that highlighted the features including pH and temperature sensors, hardware, mobile application, system’s frontend, and backend analysis, and stores the extracted information in the cloud using IoT. The system is made up of frontend data acquisition, data transmission, data processing, and reception, and is also experimentally validated to find out all possible crops that can be cultivated in a specific land with the required amount of fertilizers as well as the overall crops distribution lists.

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review.

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield

A majority of the population of developing countries is associated with agriculture directly or indirectly. The liaison of engineering technology and Sustainable Development Goals (SDGs) can build a bridge for farmers to enhance their skills regarding advancements through future generation agriculture trends. The next-generation trends include better soil preparation, intelligent irrigation systems, advanced methods of crop nutrient inspection, smart fertilizers applications, and multi-cropping practices. This work proposes a smart Decision Support System (DSS) that acquires the input parameters based on real-time monitoring to optimize the yield that realizes sustainability by improving per hectare production and lessening water seepage wastage in agribusiness. The proposed model comprises three basic units including an intelligent sensor module, smart irrigation system and controlled fertilizer module. The system has integrated sensors, cloud employing decision support layers, and networking based DSS to recommend cautions for optimum sustainable yield. The intelligent sensors module contains a temperature and humidity sensor, NPK sensor, soil moisture sensor, soil conductivity sensor, and pH sensor to transmit the statistics to the cloud over the internet via Long Range (LoRa) using Serial Peripheral Interface (SPI) communication protocol. Moreover, an android application has been developed for real-time data monitoring according to GPS location and node information (accessed remotely). Furthermore, the DSS contemplates the accessible information from sensors, past patterns, monitoring climate trends and creating cautions required for sustainable fertilizer consumption. The presented results and comparison validate the novelty of the design as it embraces smart irrigation with smart control and smart decision-making based on accurate real-time field data. It is better than existing systems as it transmits the data over the LoRa that is an open-source communication with long-range transmission ability up to several kilometres. The sensor nodes helped in advancing the yield of crops, which resulted in achieving inclusive and sustainable economic goals.

Blockchain-Enabled Smart Agricultural Knowledge Discovery System using Edge Computing

Blockchain empowered agricultural knowledge discovery system provides the secured environment for the people to store and exchange agricultural data. The integration of the Internet of Things (IoT), blockchain technology, and edge computing supports the agricultural fields to increase crop productivity and reduce the usage of natural resources. The smart decision support system provides efficient data management to the farmer to make the best decision to increase crop productivity and profit. In the agriculture field, different sensors and devices collect different data about the land then it can be sent to the server in the cloud environment. These data are highly sensitive and need to be securely stored and protected against unauthorized access. The farmers can communicate with the intelligent decision support system using the mobile app and web applications to get information about the land from domain experts. The proposed system used blockchain technology to provide privacy, security, easy accessibility, and availability of information to the people to use natural resources effectively and reduce the effect of soil and water pollution by using pesticides and fertilizer based on data stored in the knowledge base. We have implemented a knowledge discovery system using the platform hyperledger for efficient storage and sharing of sensor data which provides security and data privacy. Precision agriculture with IoT and Edge computing allows people and devices to be connected anywhere, anytime in a smart cloud environment to provide intelligent service to the farmers.

HEDONIC MODELING OF HOUSING PURCHASE/SALE DENSITY WITH URBAN CHANGE FACTORS

Abstract. Due to the industrialization in the cities, land needs have appeared in the increasing urban population. These needs have created houses with the accumulate of collective living spaces in the city. It is necessary to determine the supply-demand relationship and value of these real estates with economic importance for smart urban management systems and decision support systems in the market. The value of real estate varies according to the country in which it is located, but in general, it is affected by many factors such as spatial attributes, demographic factors, building factors, economic conditions. Depending on these factors, values and purchase-sale densities of housing also change.In this study, for prediction of housing purchase-sale density, hedonic modeling was realized with 15 features from urban change factors. Urban change factors that affect the purchase/sale of housing such as land use, demographic factors, population density and structural factors have been examined through Geographic Information System (GIS). The hedonic regression method was used for predicting the density of housing purchase/sale. As a result of the modeling, it was found as R2 = 0,85.