Analysis Support System Research Articles

Design of Financial Data Analysis and Decision Support System based on Big Data

Design of Financial Data Analysis and Decision Support System based on Big Data

Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.

Characterization of Soil and Growth Response of Rice on Different Positions along the Toposequence in Awka, Anambra State

This study examined soil characteristics and the growth response of Rice along different positions of a toposequence in Awka, Anambra State, Nigeria. The soils were delineated using the Geomorphon algorithm in the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) environments. This was then readapted to reflect the Uplands, midslopes, and Lowlands. The results show that in the uplands, bulk density increased down the profile, silt decreased down the profile, and clay fluctuated with increasing soil depth. In the lowlands, sand and silt content decreased down the profile, while clay content increased down the profile. The clay content had an inverse relationship with saturated hydraulic conductivity, decreasing the profile. In the midslope, sand content increased, and silt content decreased, while clay content was high in the topsoil, fluctuating with increasing depth. This result shows that physiographic positions critically influence the nature, properties, and variation of soils across the landscape. The soil properties, in turn, influenced the growth response of the rice plants, with particular reference to varietal variations (FARO 44, 52, 58, 59). Due to its high sand content, high runoff rate, and saturated hydraulic conductivity, the upland yielded rice plants with a lower number of tillers and height, while the reverse was the case in the lowlands. This may be attributed to Rice thriving best under swampy conditions, which are prevalent in lowland areas. The relatively low hydraulic conductivity, and higher organic matter, clay content, and water accumulation in the lowlands consequently ensure a higher yield. Generally, the swamp rice varieties (FARO 44 and 52) performed best in the lowlands, while the upland varieties (FARO 58 and 59) performed best in the midslopes.

EDAS method for circular pythagorean fuzzy with improved Dombi power aggregation operators and their application in policy analysis and decision support systems

A circular Pythagorean fuzzy set is a very dominant and reliable technique for coping with uncertain and vague information in genuine life problems because it contains the membership function, non-membership function, and radius between both grades with a condition that is the sum of the pair will be contained in the unit interval. The objective of this article is to define some improved Dombi operational laws based on circular improved Pythagorean fuzzy values. Moreover, we discuss some drawbacks of the existing Dombi aggregation operators based on circular improved Pythagorean fuzzy numbers. We also defined some improved Dombi averaging and geometric aggregation operators and their properties. Later on, a technique of evaluation based on distance from the average solution is stated by using the proposed operators. Additionally, we resolve the problem of policy analysis and decision support systems based on multi-attribute decision-making problems for initiated techniques to enhance the worth of the derived theory. Finally, we illustrate some examples for showing the supremacy and validity of the proposed theory with the help of comparative analysis between proposed ranking values and some existing ranking values.

CITYTWIN – AI-based Decision Support System for Semantic Search and Analysis of Location-based Information for Urban and Site Planning

CITYTWIN – AI-based Decision Support System for Semantic Search and Analysis of Location-based Information for Urban and Site Planning

Deep Learning Approaches for Medical Image Analysis and Diagnosis.

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way forseamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefitfrom deep learning models.

Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India.

A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research.

Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

IoHT Based Tele-Healthcare Support System for Feasibility and Perfor-mance Analysis

With the rapid development in Internet of Things (IoT) it come up with the subbranch Internet of Healthcare Things or the Internet of Medical Things. With the help of portable healthcare sensors and the wearable devices now, ambulance can be converted into critical diagnostics unit on the wheels. Monitoring the home care patient is not the just part of IoT application where doctor can remotely monitor the patient situation by the means of sensors. It requires a complete system may called as telemedicine where multiple components will play similar priority role to monitor the patient status using sensors, let the doctor connect the patient, data analytics calculate the progression, AI engine will detect the emergency and act upon. The proposed system is to design and developing centralized tele-healthcare support system and systematic analytics sharing with multiple level of user with privacy protection. This will allow different authorities to take advantages from patient generated records.

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

Dynamical analysis and decision support system of production management

Nonlinear system dynamics and feedback control theory are presented for management optimization of supply chain systems. Linearization and simplification methods are widely used in analyzing the system dynamics of supply chains because actual production models are highly complex and nonlinear systems. With advanced system dynamics, it is possible to deal directly with nonlinear dynamical problems without linear approximate methods so that the decision-makers can obtain more accurate results for systematic management strategies. This paper proposes a nonlinear system theory to explore dynamical behavior and control synthesis of production-distribution systems using Forrester’s model. A novel super-twisting sliding mode control (SWT-SMC) algorithm has been presented based on adaptation law, ensuring management optimization against disruptions. The closed-loop system stability has been guaranteed by using the Lyapunov theory. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the adaptive super-twisting sliding mode control (ASWT-SMC) algorithm. Four types of decision criteria have been employed to compare system performance between control strategies. With a superb decision scheme powered by a control algorithm, novel supply chain software can learn an ever-fluctuating production flow and anticipate the need for changes in a real market.

Development of a Big Data Analysis and Management Decision Support System for Student Mental Health in Higher Education

Abstract In recent years, the number of psychological problems occurring in college students has been increasing day by day, and timely, convenient, and accurate psychological warning is an important way to prevent college students from psychological crises. Starting from the basic needs of the system and aiming at realizing the embedding of data mining technology in the psychological management system, this paper elaborates on the design and implementation of the data mining technology module suitable for the psychological management system. The clustering algorithm-K mean algorithm of data mining is used to distinguish groups of students with different categories of psychological problems, which provides data support for the next decision-making. Through simulation experiments between the system designed in this paper and four other mental health management systems, the clustering performance and system evaluation performance of this paper’s system are better than those of the other systems. The result of mining the mental health of college students through this system shows that 82.2% of the student’s assessment results show normal, and 8.35% of the students need to focus on, and this result is not much different from that obtained from the statistical survey of a personality questionnaire, which indicates that the use of clustering analysis in the system can be an effective prediction of the mental health of college students.

Big Data Analysis and Decision Support System Based on Deep Learning

Big Data Analysis and Decision Support System Based on Deep Learning

Multimodal graph attention network for COVID-19 outcome prediction

When dealing with a newly emerging disease such as COVID-19, the impact of patient- and disease-specific factors (e.g., body weight or known co-morbidities) on the immediate course of the disease is largely unknown. An accurate prediction of the most likely individual disease progression can improve the planning of limited resources and finding the optimal treatment for patients. In the case of COVID-19, the need for intensive care unit (ICU) admission of pneumonia patients can often only be determined on short notice by acute indicators such as vital signs (e.g., breathing rate, blood oxygen levels), whereas statistical analysis and decision support systems that integrate all of the available data could enable an earlier prognosis. To this end, we propose a holistic, multimodal graph-based approach combining imaging and non-imaging information. Specifically, we introduce a multimodal similarity metric to build a population graph that shows a clustering of patients. For each patient in the graph, we extract radiomic features from a segmentation network that also serves as a latent image feature encoder. Together with clinical patient data like vital signs, demographics, and lab results, these modalities are combined into a multimodal representation of each patient. This feature extraction is trained end-to-end with an image-based Graph Attention Network to process the population graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation, and mortality. To combine multiple modalities, radiomic features are extracted from chest CTs using a segmentation neural network. Results on a dataset collected in Klinikum rechts der Isar in Munich, Germany and the publicly available iCTCF dataset show that our approach outperforms single modality and non-graph baselines. Moreover, our clustering and graph attention increases understanding of the patient relationships within the population graph and provides insight into the network’s decision-making process.

Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS

This study documents the changes in the Land Use/Land Cover (LULC) in the region of saline lakes in north Tunisia, Sahara Desert. Remote sensing data are a valuable data source in monitoring LULC in lacustrine landscapes, because variations in the extent of lakes are visible from space and can be detected on the images. In this study, changes in LULC of the salt pans of Tunisia were evaluated using a series of 12 Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) images. The images were processed with the Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The study area included four salt lakes of north Tunisia in the two regions of the Gulf of Hammamet and Gulf of Gabès: (1) Sebkhet de Sidi el Hani (Sousse Governorate), (2) Sebkha de Moknine (Mahdia Governorate), (3) Sebkhet El Rharra and (4) Sebkhet en Noual (Sfax). A quantitative estimate of the areal extent analysed in this study is 182 km × 185 km for each Landsat scene in two study areas: Gulf of Hammamet and Gulf of Gabès. The images were analysed for the period 2017–2023 on months February, April and July for each year. Spatio-temporal changes in LULC and their climate–environmental driving forces were analysed. The results were interpreted and the highest changes were detected by accuracy assessment, computing the class separability matrices, evaluating the means and standard deviation for each band and plotting the reject probability maps. Multi-temporal changes in LULC classes are reported for each image. The results demonstrated that changes in salt lakes were determined for winter/spring/summer months as detected changes in water/land/salt/sand/vegetation areas. The accuracy of the classified images was evaluated using pixel rejection probability values, which were filtered out using the ‘r.mapcalc’ module of GRASS GIS. The confidence levels were computed and visualised with a series of maps along with the error matrix and measured convergence level of classified pixels. This paper contributes to the environmental monitoring of Tunisian landscapes and analysis of climate effects on LULC in landscapes of north Africa.

Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study

BackgroundElectronic medical records (EMRs) contain a wealth of information related to breast cancer diagnosis and treatment. Extracting relevant features from these medical records and constructing a knowledge graph can significantly contribute to an efficient data analysis and decision support system for breast cancer diagnosis.MethodsAn approach was proposed to develop a workflow for effectively extracting breast cancer-related features from Chinese breast cancer mammography reports and constructing a knowledge graph for breast cancer diagnosis. Firstly, the concept layer of the knowledge graph for breast cancer diagnosis was constructed based on breast cancer diagnosis and treatment guidelines, along with insights from clinical experts. .Next, a BiLSTM-Highway-CRF model was designed to extract the mammography features, which formed the data layer of the knowledge graph. Finally, the knowledge graph was constructed by combining the concept layer and the data layer in a Neo4j graph data platform, and then applied in visualization analysis, semantic query and computer assisted diagnosis.ResultsMammographic features were extracted from a total of 1171 mammography examination reports. The overall extraction performance of the model achieved an accuracy rate of 97.16%, a recall rate of 98.06%, and a F1 score of 97.61%. Additionally, 47,660 relationships between entities were identified based on the four different types of relationships defined in the concept layer. The knowledge graph for breast cancer diagnosis was constructed after inputting mammographic features and relationships into the Neo4j graph data platform. The model was assessed from the concept layer, data layer, and application layer perspectives, and showed promising results.ConclusionsThe proposed workflow is applicable for constructing knowledge graphs for breast cancer diagnosis based on Chinese EMRs. This study serves as a reference for the rapid design, construction, and application of knowledge graphs for diagnosis and treatment of other diseases. Furthermore, it offers a potential solution to address the issues of limited data sharing and format inconsistencies present in Chinese EMR data.

Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy.

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.

Mapping Wetlands of Kenya Using Geographic Resources Analysis Support System (GRASS GIS) with Remote Sensing Data

Abstract Monitoring wetlands of Kenya is critical for analysis of environmental changes since they present unique ecosystems with special hydrological balance and biodiversity. In this study, the Landsat 8-9 OLI/TIRS satellite images for 2015-2022 were classified using GRASS GIS scripts to evaluate changes in the Lorian Swamp wetland, north-eastern Kenya. The results of the image analysis presented maps of land cover changes including wetlands. The study demonstrated technical effectiveness of the GRASS GIS for image analysis, and contributed to the environmental monitoring of African wetlands.

LEVERAGING ARTIFICIAL INTELLIGENCE AND BIG DATA FOR ADVANCED SPATIAL ANALYTICS AND DECISION SUPPORT SYSTEMS IN GEOGRAPHY

Geospatial artificial intelligence (GeoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning, data mining, and high-performance computing to extract knowledge from spatial big data. GeoAI, or geospatial artificial intelligence, has become an emerging topic and the frontier for spatial analytics in Geography. This study explores how these technologies can enhance spatial analysis and decision support systems, leading to more accurate, efficient, and sustainable outcomes. The integration of AI and Big Data facilitates improved data acquisition and management, enabling the processing of large volumes of geospatial data. The paper highlighted the applications of AI and Big Data in geography, such as optimizing urban development, environmental monitoring, and disaster management. However, it also acknowledges the challenges, including data privacy and security concerns, potential biases in algorithms, and data quality and integration issues. The paper concludes by emphasizing the need for interdisciplinary collaborations and the development of user-friendly tools and platforms to democratize the adoption of AI and Big Data applications in geography.