Knowledge-driven Decision Support Research Articles

An Ensemble Framework Augmenting Surveillance Cameras for Detecting Intruder Clusters as Potential Mobs

<p>Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a Machine Learning (ML) 3-tiers Ensemble framework, which integrates Gray Level Co-occurrence Matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.</p>

Minimizing Epistemic Uncertainty for Predictive Control of General Anesthesia

Epistemic uncertainty refers to the lack of information from knowledge and in medical field it is often paired with decision making situations and systems largely dependent on context and expertise. Our approach is uncertainty-centric and digitalizes knowledge with high certainty level, in order to improve the efficacy of knowledge-driven decision support system for anesthesia regulation. We propose to digitize some of surgical actions as part of minimizing uncertainty in closed loop control. Notable disturbances are those induced by the surgery actions, but they abide clinical protocols and semantic transformation enables their use in computers. We introduce a predictive control methodology to accommodate the augmented information of the system and evaluate its impact on the closed loop performance. Simulations of closed loop control of anesthesia for various surgery protocols indicate a high relevance and applicability of proposed approach. The proposed control structure allows the use of medical semantics in decision making process of anesthesia regulation.

Digital Twin-enabled and Knowledge-driven decision support for tunnel electromechanical equipment maintenance

Urban tunnel infrastructure plays critical roles in sustaining the wellbeing of a society. The operation of tunnels relies on a diverse range of tunnel electromechanical equipment (TEE), such as ventilation, drainage, and the lighting system. However, effectively and proactively maintaining TEE to prevent unforeseen failures using limited resources remains an unresolved challenge. The utilization of digital twin technology, which combines Building Information Modeling (BIM), Internet of Things (IoT) and Semantic Web technologies, offers a knowledge-rich environment for the development of improved maintenance strategies. This study proposes a digital twin-enabled and knowledge-driven decision support method for proactive TEE maintenance. Initially, a digital twin conceptual framework for proactive TEE maintenance is presented, which integrates a knowledge-driven approach to support decision making. Subsequently, a controlled vocabulary-based method is developed to extract and update maintenance knowledge. Finally, a novel combinatory reasoner, including a rule selection algorithm and an inference algorithm, is devised to automatically generate maintenance schemes. Based on the proposed method, a decision support tool was developed and applied to Wenyi Road Tunnel in Hangzhou, China. The results demonstrated the effectiveness of the method, which can continuously update maintenance knowledge and assist fault detection and TEE state prediction with the combinatory reasoner. Moreover, the inference efficiency achieved by our method surpasses that of traditional approaches by approximately 162 ms.

Disaster management ontology- an ontological approach to disaster management automation

The geographical location of any region, as well as large-scale environmental changes caused by a variety of factors, invite a wide range of disasters. Floods, droughts, earthquakes, cyclones, landslides, tornadoes, and cloudbursts are all common natural disasters that destroy property and kill people. On average, 0.1% of the total deaths globally in the past decade have been due to natural disasters. The National Disaster Management Authority (NDMA), a branch of the Ministry of Home Affairs, plays an important role in disaster management in India by taking responsibility for risk mitigation, response, and recovery from all natural and man-made disasters. This article presents an ontology-based disaster management framework based on the NDMA’s responsibility matrix. This ontological base framework is named as Disaster Management Ontology (DMO). It aids in task distribution among necessary authorities at various stages of a disaster, as well as a knowledge-driven decision support system for financial assistance to victims. In the proposed DMO, ontology has been used to integrate knowledge as well as a working platform for reasoners, and the Decision Support System (DSS) ruleset is written in Semantic Web Rule Language (SWRL), which is based on the First Order Logic (FOL) concept. In addition, OntoGraph, a class view of taxonomy, is used to make taxonomy more interactive for users.

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Climate change is the most serious causes and has a direct impact on biodiversity. According to the world’s biodiversity conservation organization, reptile species are most affected since their biological and ecological qualities are directly linked to climate. Due to a lack of time frame in existing works, conservation adoption affects the performance of existing works. The proposed research presents a knowledge-driven Decision Support System (DSS) including the assisted translocation to adapt to future climate change to conserving from its extinction. The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species. However, the framework demonstrates the huge difference in the estimated significance of climate change, the model strategy helps to recognize the probable risk of threatened species translocation to future climate change. The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

Information Processing and Data Analytics for Decision Making

Decision making is required by all organizations; however, the decision making styles may differ. ost commonly used decision styles include: (a) Autocratic (b) Democratic (c) Consensus and (d) Participatory. With the Globalization and expansion of businesses, professionals have become highly dependent upon the technology to support the decision making process and decision support systems have come-up as a fastest growing discipline. Present work discusses the evolution of computerized decision support, considering the: (a) Model Driven (b) Data Driven (c) Communication Driven (d) Document Driven and (e) Knowledge Driven decision support systems. All three different business levels: Operational, Tactical and Strategic have been considered in the present work to review the development of decision support systems. The traditional data analysis based approaches have been compared with the latest data analytics approaches including the social media analytics and web analytics. Examples from the different industry sectors have been incorporated for better illustrations of decision support.

A knowledge-driven decision support system for remote medical management.

Residential medical digital technology is an emerging discipline combining computer network technology and medical research. Based on the idea of knowledge discovery, this study was designed to construct a decision support system for remote medical management, analyze the need for utilization rate calculations and obtain relevant modeling elements for system design. Specifically, the model constructs a design method for a decision support system for the healthcare management of elderly residents through the use of a utilization rate modeling method based on digital information extraction. In the simulation process, the utilization rate modeling and system design intent analysis are combined to obtain the relevant functions and morphological characteristics that are essential to the system. Using regular usage slices, a higher-precision non-uniform rational B-spline (NURBS) usage rate can be fitted and a surface model with better continuity can be constructed. The experimental results show that the deviation of the NURBS usage rate generated by the boundary division from the original data model can reach test accuracies of 83, 87 and 89%, respectively. It is shown that the method can effectively reduce the modeling error caused by the irregular feature model in the process of modeling the utilization rate of digital information, and that it can ensure the accuracy of the model.

Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.

A decision support system for DM algorithm selection based on module extraction

Data mining techniques are needed in various fields. However, most data researchers do not have sufficient knowledge and experience. Due to the significant number of algorithms and parameters for data analysis, intuition-based decisions can’t lead to optimal solutions. Ontology is widely adopted to build knowledge-driven decision support systems since it is suited to encapsulate the concepts and relationships of terms associated with various domains. It is suitable for capturing knowledge by computers, allowing sharing and reusing it whenever necessary. All concepts and relationships about data analysis can be described using Web Ontology Language (OWL). Its reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend suitable solutions for a specific analysis task by considering the data characteristics and task requirements. In the previous work, we have developed a system that describes comprehensive knowledge and logical internal relationships about data mining. We found that the reasoning and query complexity is high in the large size ontology, so this paper focuses on the modeling and implementation of an ontology-based decision support system for data mining algorithm selection that contains a new sub-ontology extraction method. Extracting effective content can significantly reduce the size of the ontology. This extraction method can improve query efficiency on the premise of ensuring the quality of information.

Developing a Knowledge-Driven Decision Support System for University/College Selection Problem in Kuwait

Developing a Knowledge-Driven Decision Support System for University/College Selection Problem in Kuwait

Assessment of actionability of cancer genomic testing panels based on a structured clinical trial knowledge base.

6533 Background: Today’s oncologist is responsible for choosing appropriate cancer genomics tests to inform patient treatment from multiple available platforms, weighing cost, availability, sensitivity and specificity, and clinical actionability. Knowledge-driven clinical decision support tools can assist clinicians in choosing the panel that is most informative in a given clinical space. Methods: Using a queryable knowledgebase of >1800 active clinical trials containing structured eligibility criteria curations for diagnosis and genomic alterations, we compared two CLIA-regulated genomic panels for clinical actionability over the landscape of solid, breast, and lung cancer clinical trials. Results: The larger panel (73 genes) was more actionable than the smaller panel (62 genes) in the breast cancer (10x more trials returned) and solid tumor (2.7x more trials returned) clinical trial space, while the smaller panel returned 1.2x more trials in the lung cancer space (see table). Conclusions: This analysis demonstrates that patient diagnosis has a significant effect on the potential clinical actionability of a given genomic panel. Further, this analysis demonstrates the clinical utility of knowledge-driven clinical decision support tools for test selection, especially given the often-limited tumor sample available, cost of genomic panel testing, and continuously shifting trial landscape. [Table: see text]

Knowledge-Driven Decision Support System Based on Knowledge Warehouse and Data Mining by Improving Apriori Algorithm with Fuzzy Logic

Knowledge-Driven Decision Support System Based on Knowledge Warehouse and Data Mining by Improving Apriori Algorithm with Fuzzy Logic

Semantic Network Based Modelling to Design a Knowledge-Driven Decision Support Tool for Floral Diversity

Semantic Network Based Modelling to Design a Knowledge-Driven Decision Support Tool for Floral Diversity

OntoDiabetic: An Ontology-Based Clinical Decision Support System for Diabetic Patients

Clinical decision support systems (CDSS) assist medical practitioners in their daily work, thereby enhancing the quality of care given to a patient. It supports them in the decision-making process and suggests appropriate treatments. The use of the ontology to build knowledge-driven decision support systems is widely adopted. Ontology is best suited to encapsulate the concepts and relationships of terms associated with the medical domain. It is suitable for capturing medical knowledge in a formal way, allowing sharing and reusing it whenever necessary. All concepts and relationships detailed in clinical guidelines can be implemented using Web Ontology Language (OWL). The reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend the suitable treatment for a patient by considering the current medical status of the patient. OntoDiabetic, an ontology-based decision support system is developed to assess the risk factors and provide appropriate treatment suggestions for diabetic patients. This paper focuses on the modeling and implementation of clinical guidelines using OWL2 rules and the reasoning process of the OntoDiabetic system. The case study is conducted for patients having the risk of overt cardiovascular disease, diabetic nephropathy and hypertension in primary health centers of Oman.

Data Mining Approach to Decision Support in Social Welfare

Knowledge discovery and data-mining techniques have the potential to provide insights into data that can improve decision making. This paper explores the use of data mining to extract patterns from data in the domain of social welfare. It discusses the application of the Integrated Knowledge Discovery and Data Mining process model (IKDDM) a social welfare programme in Jamaica. Further, it demonstrates how the knowledge acquired from the data is used to develop a knowledge driven decision support system (DSS) in the PATH CCT programme. This system was successfully tested in the domain showing over 94% accuracy in the comparative decisions produced.

A model-driven ontology approach for manufacturing system interoperability and knowledge sharing

The requirements for the interoperability of semantics and knowledge have become increasingly important in Product Lifecycle Management (PLM), in the drive towards knowledge-driven decision support in the manufacturing industry. This article presents a novel concept, based on the Model Driven Architecture (MDA). The concept has been implemented under the Interoperable Manufacturing Knowledge Systems (IMKS) project in order to understand the extent to which manufacturing system interoperability can be supported using radically new methods of knowledge sharing. The concept exploits the capabilities of semantically well-defined core concepts formalised in a Common Logic-based ontology language. The core semantics can be specialised to configure multiple application-specific knowledge bases, as well as product and manufacturing information platforms. Furthermore, the utilisation of the expressive ontology language and the generic nature of core concepts help support the specification of system mechanisms to enable the verification of knowledge across multiple platforms. An experimental demonstration, using a test case based on the design and manufacture of an aerospace part, has been realised. This has led to the identification of several benefits of the approach, its current limitations as well as the areas to be considered for further work.

Extending product lifecycle management for manufacturing knowledge sharing

Product lifecycle management provides a framework for information sharing that promotes various types of decision-making procedures. For product lifecycle management to advance towards knowledge-driven decision support, then this demands more than simply exchanging information. There is, therefore, a need to formally capture best practice through-life engineering knowledge that can be fed back across the product lifecycle. This article investigates the interoperable manufacturing knowledge systems concept. Interoperable manufacturing knowledge systems use an expressive ontological approach that drives the improved configuration of product lifecycle management systems for manufacturing knowledge sharing. An ontology of relevant core product lifecycle concepts is identified from which viewpoint-specific domains, such as design and manufacture, can be formalised. Essential ontology-based mechanisms are accommodated to support the verification and sharing of manufacturing knowledge across domains. The work has been experimentally assessed using an aerospace compressor disc design and manufacture example. While it has been demonstrated that the approach supports the representation of disparate design and manufacture perspectives as well as manufacturing knowledge feedback in a timely manner, areas for improvement have also been identified for future work.

A Multi-Agent Decision Support Architecture for Knowledge Representation and Exchange

Organizations rely on knowledge-driven systems for delivering problem-specific knowledge over Internet-based distributed platforms to decision-makers. Recent advances in systems support for problem solving have seen increased use of artificial intelligence (AI) techniques for knowledge representation in multiple forms. This article presents an Intelligent Knowledge-based Multi-agent Decision Support Architecture” (IKMDSA) to illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create, exchange and use knowledge in decision support. IKMDSA integrates knowledge discovery and machine learning techniques for the creation of knowledge from organizational data; and knowledge repositories (KR) for its storage management and use by intelligent software agents in providing effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.