Evidential Reasoning Research Articles

Interpretable diagnosis of breast lesions in ultrasound imaging using deep multi-stage reasoning

Objective.Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis.Approach.To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions.Main results.To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255.Significance.This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

A fuzzy evidential reasoning-based approach for assessing the navigation obstruction risk of shipwrecks in the Yangtze river

ABSTRACT This paper aims to evaluate the navigation obstruction risk represented by shipwrecks in shallow waters based on a fuzzy evidential reasoning approach. The purpose of this approach is to establish a hierarchical decision-making framework that considers ship conditions, traffic environment, distance from sensitive areas, and natural environment; to derive the belief rule base using the IF-THEN rule with a belief structure; and to obtain the crisp values of navigation obstruction risk of shipwrecks via evidential reasoning. This approach is applied to the navigation obstruction risk assessment of 10 historical shipwrecks in the Yangtze River, and the evaluation results are consistent with historical data. Consequently, this approach can be used for assisting in shipwreck salvage decision-making in navigable waters.

Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT

Positron Emission Tomography/Computed Tomography (PET/CT) using Fluorodeoxyglucose (FDG) is an important imaging modality for assessing treatment outcomes in patients with pulmonary malignant neoplasms undergoing radiation therapy. However, distinguishing between benign post-radiation changes and residual or recurrent malignancies on PET/CT images is challenging. Leveraging the potential of artificial intelligence (AI), we aimed to develop a hybrid fusion model integrating radiomics and Convolutional Neural Network (CNN) architectures to improve differentiation between benign post-radiation changes and residual or recurrent malignancies on PET/CT images. We retrospectively collected post-radiation PET/CTs with identified labels for benign changes or residual/recurrent malignant lesions from 95 lung cancer patients who received radiation therapy. Firstly, we developed separate radiomics and CNN models using handcrafted and self-learning features, respectively. Then, to build a more reliable model, we fused the probabilities from the two models through an evidential reasoning approach to derive the final prediction probability. Five-folder cross-validation was performed to evaluate the proposed radiomics, CNN, and fusion models. Overall, the hybrid fusion model outperformed the other two models in terms of sensitivity, specificity, accuracy, and the area under the curve (AUC) with values of 0.67, 0.72, 0.69, and 0.72, respectively. Evaluation results on the three AI models we developed suggest that handcrafted features and learned features may provide complementary information for residual or recurrent malignancy identification in PET/CT.

Prediction of human error probability in helicopter to ship transfer operation under an evidential reasoning extended CREAM approach.

Although the helicopter to ship transfer operation is rarely applied in ports around the world, some of the hub ports have encouraged it. The operational process poses challenges to safety due to the nature of the work to be completed by the ship crew. This article performs a systematic human error prediction to ensure safe helicopter to ship transfer operations under different circumstances since the human factor is becoming a significant contributor to maritime accidents. To accomplish this, the article addresses a cognitive reliability and error analysis method (CREAM) under the evidential reasoning (ER) approach. In the proposed approach, the CREAM provides a comprehensive human error prediction tool and ER is capable of supporting experts' judgment in decision-making. The findings of the research show that 'Secure the loose objects within or adjacent to the operating area' poses a high human error probability (4.50E-02) during helicopter to ship transfer operations.

Optimal scheduling of green heating systems in buildings considering economy and exergy efficiency

Green heating in buildings is of great significance to global energy conservation and emission reduction, and the design of optimal scheduling models is the key to achieve cost reduction and efficiency improvement in heating systems. For the building heating system containing multiple heterogeneous energy sources such as solar energy, air energy and electricity, a multi-objective optimal scheduling model is established with the goal of maximizing the exergy efficiency and minimizing the economic cost of the system, taking into account the quality difference of energy in the conversion process. Regarding the nonconvex and nonlinear constraints in the scheduling model, the incremental linearization method and Big-M method are used to linearize the transformation, and obtain the multi-objective mixed-integer linear programming model. Further, the ε-constraint method is used to solve the Pareto frontier of multi-objective optimization and the technique for order preference by similarity to ideal solution (TOPSIS) method is used to make decision. Finally, the simulation study of the model is carried out, and the results show that the proposed multi-objective optimal scheduling can balance the system operation economy and exergy efficiency compared with the belief rule base and evidential reasoning (BRB-ER) method, and the system’s daily economic cost are reduced by 9.66 %, 9.94 % and 17.24 %, respectively, and the system’s average daily coefficient of performance (COP) are improved by 4.66 %, 14.05 % and 13.22 %, respectively, on three typical days, it is verified that the proposed optimal scheduling model has better economy and exergy efficiency. The proposed multi-objective optimal scheduling model introduces exergy efficiency rather than energy efficiency as one of the optimization objectives of the heating system and reasonably solves the optimization model, which can provide a new idea and reference for the multi-objective optimal scheduling of the green heating system in buildings.

A conceptual risk modelling for cargo tank fire/explosion in chemical tanker by using Evidential Reasoning -SLIM and Bayesian belief network approach

The transportation of hazardous cargoes, particularly in chemical tankers, represents significant risks such as fire and explosion, which can have catastrophic consequences for both human life, commodity and maritime environment. This article presents a conceptual risk modelling for cargo tank fire/explosion incidents in chemical tanker ships under the Evidential Reasoning (ER) - Success likelihood index method (SLIM) approach with the Bayesian belief network (BBN) to provide a practical tool for assessing and managing the associated risks. Whilst the ER-SLIM approach offers a systematic human error probability (HEP) prediction and handling uncertainty in risk, allowing for the incorporation of expert judgments and data-driven information, the BBN provides a probabilistic graphical model to represent the causal relationships among various risk factors. The result shows that the risk of cargo tank fire/explosion during the cargo tank cleaning operation is calculated as 2.83E-02. The findings of the research including consequences analysis provide valuable insights for decision-makers, safety managers, superintendents, ship masters and officers in the maritime industry to prioritize risk management strategies and enhance the safety of chemical tanker operations.

Risk Evaluation and Software Development of Urban Natural Gas Pipelines Based on Bayesian Networks

Based on the urban natural gas pipeline accident statistics and semi-quantitative risk evaluation index system, this paper applies Bayesian network to establish a network model between various types of risk factors and the risk of natural gas pipeline failure. The EM algorithm was used to learn from the statistical accident data to obtain the parameters of the model. Based on the principle of evidential reasoning in reverse, the probability of occurrence of all risk indicators can be obtained when the probability of occurrence of urban natural gas pipeline accidents is 100%, the index weight is obtained by normalizing the occurrence probability. On this basis, this paper develops an efficient urban natural gas pipeline integrity risk identification and management software. The software can realize the basic data management of urban natural gas pipeline system, pipeline relative risk value calculation, pipeline risk level calculation and other functions, and the results are visualized. Finally, the practicability and effectiveness of the model and software are verified by a case of natural gas pipeline evaluation in a block.

A New Student Performance Prediction Method Based on Belief Rule Base with Automated Construction

Student performance prediction (SPP) is a pivotal task in educational analytics, enabling proactive interventions and optimized resource allocation by educators. Traditional SPP models are often hindered by their complexity and lack of interpretability. This study introduces a novel SPP framework, the Belief Rule Base with automated construction (Auto–BRB), designed to address these issues. Firstly, reference values are derived through data mining techniques. The model employs an IF–THEN rule-based system integrated with evidential reasoning to ensure both transparency and interpretability. Secondly, parameter optimization is achieved using the Projected Covariance Matrix Adaptive Evolution Strategy (P–CMA–ES), significantly enhancing model accuracy. Moreover, the Akaike Information Criterion (AIC) is then applied to fine-tune the balance between model accuracy and complexity. Finally, case studies on SPP have shown that the Auto–BRB model has an advantage over traditional models in terms of accuracy, while maintaining good interpretability. Therefore, Auto–BRB has excellent application effects in educational data analysis.

Evidential reasoning rule with dynamic reliability for performance assessment of wireless sensor network

The performance of wireless sensor network (WSN) continues to change over time, and it is required to track the WSN performance accurately. In this paper, a new assessment model known as the evidential reasoning rule with dynamic reliability (ERr-DR) is proposed. In the ERr-DR, the evidence reliability evolves from the traditional static value to a dynamic value, which can reveal the impact of external noise on network performance reasonably. As an effective extension of traditional ER rule, the ERr-DR has excellent modeling ability. Furthermore, by analysing the physical meanings of model parameters in detail, an optimization objective with targeted constraints is established. Compared with the existing optimization strategies, this not only improves the output accuracy of ERr-DR, but also maintains the interpretability of assessment results. Finally, A case study of a simulated WSN scenario validates the effectiveness of the proposed model.

Multiple factors-based damage level assessment method of concrete structures based on evidential reasoning and particle swarm optimization

Previous damage prediction methods of concrete structures usually fall into subjective and unreasonable assessments, which are usually limited to the special application scene with complex physical modeling depending on professional knowledges and experience. To overcome the above limitations, a multiple factors-based damage level assessment method of concrete structures is proposed based on evidential reasoning and particle swarm optimization. According to the method, multi-source data can be integrated for damage rules generation and quantitative damage level assessment automatically. The number and types of the used multi-source data can be chosen freely according to the special application scene. Finally, multi-source data including the normal strain in X-direction, normal strain in Y-direction, and the corresponding shear strain of two concrete structures under cyclic loads are utilized to verify the developed method. The results show that the damage level assessment results match well with the corresponding experimental results, which has a high reliability index based on the developed method. The ability of the method with good generality and flexibility is verified, which shows that the method can be used to achieve the reasonable damage level assessment of concrete structures.

Network Security Prediction of Industrial Control Based on Projection Equalization Optimization Algorithm.

This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture prediction model, ensuring the secure operation and prediction of the ICS. This model first integrates various information from the ICS to determine its network security posture value. Subsequently, through ER iteration, information fusion occurs and serves as an input for the BRB prediction model, which necessitates initial parameter setting by relevant experts. External factors may influence the experts' predictions; therefore, this paper proposes the Projection Equalization Optimization (P-EO) algorithm. This optimization algorithm updates the initial parameters to enhance the prediction of the ICS network security posture through the model. Finally, industrial datasets are used as experimental data to improve the credibility of the prediction experiments and validate the model's predictive performance in the ICS. Compared with other methods, this paper's prediction model demonstrates a superior prediction accuracy. By further comparing with other algorithms, this paper has a certain advantage when using less historical data to make predictions.

18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.

Modal reduction principles: a parametric shift to graphs

Graph-based frames have been introduced as a logical framework which internalises an inherent boundary to knowability (referred to as ‘informational entropy’), due, e.g. to perceptual, evidential or linguistic limits. They also support the interpretation of lattice-based (modal) logics as hyper-constructive logics of evidential reasoning. Conceptually, the present paper proposes graph-based frames as a formal framework suitable for generalising Pawlak's rough set theory to a setting in which inherent limits to knowability exist and need to be considered. Technically, the present paper establishes systematic connections between the first-order correspondents of Sahlqvist modal reduction principles on Kripke frames, and on the more general relational environments of graph-based and polarity-based frames. This work is part of a research line aiming at: (a) comparing and inter-relating the various (first-order) conditions corresponding to a given (modal) axiom in different relational semantics; (b) recognising when first-order sentences in the frame-correspondence languages of different relational structures encode the same ‘modal content’; (c) meaningfully transferring relational properties across different semantic contexts. The present paper develops these results for the graph-based semantics, polarity-based semantics, and all Sahlqvist modal reduction principles. As an application, we study well known modal axioms in rough set theory (such as those corresponding to seriality, reflexivity, and transitivity) on graph-based frames and show that, although these axioms correspond to different first-order conditions on graph-based frames, their intuitive meaning is retained. This allows us to introduce the notion of hyperconstructivist approximation spaces as the subclass of graph-based frames defined by the first-order conditions corresponding to the same modal axioms defining classical generalised approximation spaces, and to transfer the properties and the intuitive understanding of different approximation spaces to the more general framework of graph-based frames. The approach presented in this paper provides a base for systematically comparing and connecting various formal frameworks in rough set theory, and for the transfer of insights across different frameworks.

Understanding Epistemic Injustice as Contributory Injustice

This paper offers some further support to Federico Picinali’s argument, in «Evidential Reasoning, Testimonial Injustice and the Fairness of the Criminal Trial», that a trial is unfair when assessments of relevance and probative value includes an epistemic injustice, namely a testimonial injustice. It has been argued that there are barriers to establishing testimonial injustice in specific cases, such as the ones Picinali surveys. This paper argues that even if we accept that there are concerns about establishing the occurrence of a testimonial injustice in the cases Picinali identifies, we can reformulate the epistemic injustice that renders the trial unfair as a contributory injustice. Reformulating the epistemic injustice as a contributory injustice evades the concerns we might have with establishing testimonial injustice allowing Picinali’s broad argument that a trial is unfair when an assessment of evidence includes an epistemic injustice—contributory or testimonial—to remain intact. This reformulation also offers new propositions on how to combat epistemic injustice.

Performance evaluation of complex systems based on hierarchical evidential reasoning rule considering disturbances

Performance evaluation of complex systems based on hierarchical evidential reasoning rule considering disturbances

A multi-method examination of barriers to traceability in Industry 5.0-enabled digital food supply chains

PurposeRecent worldwide developments have altered how businesses operate. As a result, when making business decisions, the emphasis for many industries has shifted towards digital adoption to ensure sustainability, and the food supply chain is no exception. However, a substantial gap exists in assessing the barriers to a digitised food supply chain enabled by Industry 5.0 technologies. This study strives to bridge the gap by identifying and assessing the barriers to improved traceability.Design/methodology/approachFor this study, a mixed method approach was used encompassing both qualitative and quantitative techniques, including an online survey, exploratory factor analysis (EFA), and the fuzzy evidential reasoning approach (FERA). The literature survey and expert opinion first yielded a list of 18 barriers, which were subsequently examined using EFA. As a result, four barriers were removed. The remaining 14 barriers were then assessed using FERA from the perspectives of the Technology, Organisation and Environment (TOE) framework. Further, a sensitivity analysis was performed to test the model’s reliability.FindingsThe present study resulted in the prioritisation of barriers from the TOE perspective. According to the findings, the top three barriers that impede the traceability of Industry 5.0-enabled digital food supply chains are Limited Digital and Physical Infrastructure, Inadequate Capital Investment, and the Intricate Supply Chain Framework.Research limitations/implicationsThe findings from this research will prove valuable for decision-makers, practitioners and policymakers in developing methods for improving traceability within the digital food supply chain. Concerned stakeholders may use the findings to identify and take immediate action for better decision-making.Originality/valueThis study’s originality lies in its position as one of the first to identify and examine the challenges to better traceability in an Industry 5.0-enabled digital food supply chain. It also adds value by broadening the TOE framework’s scope in the Industry 5.0-enabled digital food supply chain context.

Adaptive dynamic fire danger evaluation of logistics warehouses with fusion of evidential reasoning and smart optimization

Considering the background that the current fire risk assessment methods often rely on subjective assessments based on professional knowledge and experience, they are usually limited to the special fire scene. Given the lack of effective tools for assessing fire risks in logistics warehouses across varying scenes, this paper aims to propose a multi-source data-driven, adaptive dynamic fire danger evaluation method. The method is established based on the fusion strategy of evidential reasoning and artificial fish swarm algorithm. According to the method, rules of danger evaluation levels related to each attribute of fire factors can be generated automatically without necessary of professional knowledges and experiences. The lightweight multi-source data at this moment can be fused to evaluate current fire danger level directly, which does not require training on extensive datasets in advance. Finally, multi-source data including temperature and CO concentration in two fire scenes of one logistics warehouse at different times are utilized to support the ability of the method. The results show that the estimated fire source location according to the center of domain with the evaluated highest danger level matches well with the real fire source location. In conclusion, the main contribution of the research is that the reasonable dynamic danger level of the logistics warehouse fire with time variation can be evaluated automatically. The method has an excellent generality, which is not limited to the specific fire scene. The danger zone of the logistics warehouse under sudden fires can be determined adaptively, which can support the fire prevention and fire safety countermeasures of logistics warehouses.

A novel evidence reasoning-based RUL prediction method integrating uncertainty information

The remaining useful life (RUL) prediction plays an essential role in maintenance decision making to ensure safe operation of mechanical equipment. However, it still remains challenges to mine and utilize the uncertainty information hidden behind the running data and to enhance the interpretability of the prediction process, which leads to inaccurate and unreasonable prediction results. Therefore, a novel evidence reasoning-based RUL prediction method integrating uncertainty information is proposed to overcome these limitations. First, a sample casting method is presented to transform the historical sample set of an input feature sequence into the corresponding referential life evidence matrix (RLEM), achieving almost lossless transformation from RUL about the input feature sequence to life evidence. Second, the real-time samples of multiple input features activate their corresponding RLEMs to obtain the activated life evidence, and use evidence reasoning (ER) rule to fuse these activated life evidence to ultimately obtain integrated life evidence. Then, to further improve the accuracy of integrated life evidence, the reliability of activated life evidence is calculated online through sample entropy method, and the weight of life evidence is optimized offline using genetic algorithm. Finally, the integrated life evidence is transformed into predicted values of RUL based on utility theory. Two case studies on rolling bearing and hydrogen fuel cell show the superiority of the proposed RUL prediction method.

Risk assessment of achieving greenhouse gas emission reduction target in the maritime industry

To mitigate climate change, the International Maritime Organization (IMO) has mandated a 50% reduction in shipping-related greenhouse gas emissions by 2050 compared to 2018. While short-term measures are agreed, several barriers remain in achieving this emission reduction target. Existing research only focuses on specific scenarios and ignores comprehensive risks that influence the target realization. Hence, this study fills the research gap by identifying 28 potential risks and prioritizing the most threatening risks using the Political, Economic, Social, Technical, Legal, and Environmental framework. Furthermore, risk assessment on the target achievement is conducted to prioritize risk categories and risk factors via a combination of Evidential Reasoning and Rule-based Bayesian network. Finally, sensitivity analysis is performed to validate the results. The findings show that the categories, in the descending order of their importance, are economic, political, environmental, legal, social, and technological risk. In addition, the results reveal that “high low-carbon fuel prices” is the most pivotal risk under the economic category while notable political, environmental, legal, social, and technological risks are also recognized, each presenting distinct uncertainties in the decarbonization process. This study offers useful insights and strategic recommendations for stakeholders, aiding the transition to low-carbon maritime transportation.