Automatic Rule Research Articles

Dune Morphology Classification and Dataset Construction Method Based on Unmanned Aerial Vehicle Orthoimagery.

Dunes are the primary geomorphological type in deserts, and the distribution of dune morphologies is of significant importance for studying regional characteristics, formation mechanisms, and evolutionary processes. Traditional dune morphology classification methods rely on visual interpretation by humans, which is not only time-consuming and inefficient but also subjective in classification judgment. These issues have impeded the intelligent development of dune morphology classification. However, convolutional neural network (CNN) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. Therefore, this paper summarizes five typical dune morphologies in the deserts of western Inner Mongolia, which can be used to define and describe most of the dune types in Chinese deserts. Subsequently, field surveys and the experimental collection of unmanned aerial vehicle (UAV) orthoimages for different dune types were conducted. Five different types of dune morphology datasets were constructed through manual segmentation, automatic rule segmentation, random screening, and data augmentation. Finally, the classification of dune morphologies and the exploration of dataset construction methods were conducted using the VGG16 and VGG19 CNN models. The classification results of dune morphologies were comprehensively analyzed using different evaluation metrics. The experimental results indicate that when the regular segmentation scale of UAV orthoimages is 1024 × 1024 pixels with an overlap of 100 pixels, the classification accuracy, precision, recall, and F1-Score of the VGG16 model reached 97.05%, 96.91%, 96.76%, and 96.82%, respectively. The method for constructing a dune morphology dataset from automatically segmented UAV orthoimages provides a reference value for the study of large-scale dune morphology classification.

Towards explainable traffic signal control for urban networks through genetic programming

The increasing number of vehicles in urban areas draws significant attention to traffic signal control (TSC), which can enhance the efficiency of the entire network by properly switching the phases of each signalized intersection. Fixed and max-pressure methods are commonly used in TSC systems owing to their high simplicity and good interpretability, but they respectively lack dynamic adaptability and automatic rule generation, possibly leading to low solution accuracy in complicated traffic environments. Meanwhile, meta-heuristic and black-box learning methods meet challenges in practice such as extensive computational time and poor interpretability. To this end, this paper proposes a new TSC method based on Genetic Programming (GP) to generate descriptive score rules automatically for switching phases of all signalized intersections in an urban transportation network. In the proposed method, switching phases of each signalized intersection type is formulated as a symbolic regression problem, and effective primitives are defined to facilitate GP to solve the problem. Experiments have been conducted on both synthetic and real-world networks. The results have validated the effectiveness of our proposed GP based method compared to several state-of-the-art TSC methods in terms of accuracy and interpretability.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Digital twin enhanced agile design of ship pipeline systems

The shipbuilding industry plays a pivotal role in national strategic security and economic development, and one critical challenge is the pipeline layout design problem. It predominantly relies on designers’ subjective experience, and it is marked by a lack of efficient knowledge sharing and the absence of smart pipe routing algorithms. This paper proposes an agile design system, which integrates ship pipeline design knowledge management, semi-automatic design that involves frequent interaction with human designers, and automatic rule checking. The framework is refined by digital twin concepts, facilitating close collaboration between physical and digital systems. The paradigm shift holds the potential to substantially enhance the efficiency of ship pipeline layout design, while concurrently reducing the reliance on manual labor.

Decision Tree based Data Modelling for First Detection of Thalassemia Major

Thalassemia is an inherited blood disease which lacks hemoglobin, the protein that is carrying oxygen to the body. The severe one is called Thalassemia Major which needs special care about blood transfusion. The use of rule-based method to create an inference as the first diagnosis of Thalassemia Major is not effective as rules have to be achieved from long interview with the medical personnel. This research aims to create a model based on decision tree for first detection of Thalassemia Major. The dataset is obtained by interview of Thalassemia symptoms and primary data of medical records from a hospital. Classical decision tree models used are ID3, C4.5 and CART. The models are evaluated by Train-Test Split consists of 70% training and 30% testing data and k-Fold Validation for checking model’s overfitting or underfitting. The output of this research is a final tree model from the best performance of decision tree models. The final result shows that C4.5 has the best performance with accuracy 100% and not overfitting or underfitting. Also, C4.5 performs feature selections to its tree modeling to simplify the inference. In brief, decision tree based modeling is effective to be used as first detection of Thalassemia Major by interview symptoms with generating automatic rules from its tree model.

Smart Agriculture system based on IoT reference architecture and service choreography

This paper presents a monitoring, control, and management system that allows the interoperability of multiple smart gateways in a greenhouse to bolster automatic decision making on water supplied to the crops in real-time. The system consists of several sensor nodes interconnected by Zigbee to measure ambient and soil parameters to control the greenhouse irrigation process. In addition, a web service is included to help the system manager to visualize and operate the greenhouse on a web platform. The system also supports human decision making to cope with undesirable and unexpected environmental events. Smart gateways enable data reading, storage and edge computing using automatic rules to manage actions in the greenhouse, as well as data transmission for centralized processing in cloud services. The service communication system layer is supported by choreography middleware for messaging distribution, which enables agile scalability. This research proposes a hybrid scheme and compares to a centralized scheme. The latency times in the choreography system were analyzed to find a suitable solution adapted to infrastructure deployment features. Numerical results shows that such an adaptive deployment of the irrigation services among edge and fog layers can reduce the computational load on the gateways, improve system response times, and enhance device performance.

A manually annotated corpus in French for the study of urbanization and the natural risk prevention

Land artificialization is a serious problem of civilization. Urban planning and natural risk management are aimed to improve it. In France, these practices operate the Local Land Plans (PLU – Plan Local d’Urbanisme) and the Natural risk prevention plans (PPRn – Plan de Prévention des Risques naturels) containing land use rules. To facilitate automatic extraction of the rules, we manually annotated a number of those documents concerning Montpellier, a rapidly evolving agglomeration exposed to natural risks. We defined a format for labeled examples in which each entry includes title and subtitle. In addition, we proposed a hierarchical representation of class labels to generalize the use of our corpus. Our corpus, consisting of 1934 textual segments, each of which labeled by one of the 4 classes (Verifiable, Non-verifiable, Informative and Not pertinent) is the first corpus in the French language in the fields of urban planning and natural risk management. Along with presenting the corpus, we tested a state-of-the-art approach for text classification to demonstrate its usability for automatic rule extraction.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Learning Ship Activity Patterns in Maritime Data Streams: Enhancing CEP Rule Learning by Temporal and Spatial Relations and Domain-Specific Functions

Maritime surveillance systems are of particular importance for the smooth and safe operation of maritime traffic. Such systems must efficiently analyze the continuous stream of incoming ship movement data to provide an up-to-date picture of the situation at sea. Complex Event Processing (CEP) is a software technology dedicated to the analysis of data streams in real time and seems to be promising for maritime surveillance as well. CEP is based on rules that usually have to be formulated manually by domain experts. Recently, several approaches of learning CEP rules have been proposed. However, all these approaches are limited to standard CEP rule languages which are not well suited for describing maritime situations. In this paper, we extend Bat4CEP, an innovative bat-inspired approach to automatic CEP rule learning, to meet the specific needs of the maritime domain. In particular, we extend the rule language of Bat4CEP to include temporal-spatial operators and domain-specific functions. This extended rule language makes it possible to express complex maritime facts in a simple and understandable form. Furthermore, it is shown how the proposed rule language extensions can be integrated into the Bat4CEP learning approach. The effectiveness of the approach is demonstrated through extensive experiments with real maritime data streams.

Overview of trials on artificial intelligence algorithms in breast cancer screening – A roadmap for international evaluation and implementation

Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK’s National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders’ meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme.It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration.

REUSE OF INFORMATION BASED ON THE INTERPRETATION OF KNOWLEDGE

Recently, much attention has been paid to the creation of knowledge bases that contain millions of facts about various objects of the real world. One of the key aspects of knowledge management is the reuse of previously acquired knowledge. The subject of research is the processes of knowledge reuse and the creation of software systems based on knowledge bases. Knowledge interpretation is one approach to knowledge reuse, which consists in deriving new knowledge based on existing facts in the knowledge base. The purpose of the study is to increase the efficiency of knowledge reuse in software systems based on knowledge bases due to automatic rule extraction. To achieve the goal, the following tasks were solved: approaches to structuring the facts available in the database were considered, a qualitative analysis of the possibility of applying automatic methods of rule construction and derivation was carried out. The task of predicting the connection between a pair of entities, which determines the presence of a relationship for facts, is considered. A generalized approach to the presentation of facts is proposed, which allows the use of efficient rule-finding algorithms. The following methods are used to solve the given problem: the algebra of finite predicates and predicate operations for knowledge representation, methods for predicting the connection between a pair of entities based on representative learning for automatically obtaining rules. The following results were obtained: an approach to rule formation was considered, which allows structuring existing facts as a set of binary predicates and applying automatic methods of rule construction and derivation. It is concluded that the limitation of knowledge reuse is the structure of the knowledge base and the software used to support it. The article formulates the principles of building specific concentrator predicates for the representation of attributes, which allows generalizing the predicate representation of facts and applying automatic methods of rule extraction, which increases the efficiency of knowledge reuse. Conclusions: the application of the method and mechanism of identification based on predicate operations and specific predicates, which automatically extracts attributes from the knowledge base, together with the quality assessment of the derived rules, made it possible to propose a generalized approach for presenting facts and use effective rule search algorithms, which allows to increase the efficiency of reuse knowledge in software systems.

Time expression as update operations: Normalizing time expressions via a distantly supervised neural semantic parser

Existing approaches to normalize time expressions highly rely on expert-engineered rules or grammars, which are labor-intensive and difficult to scale to different scenarios. In this article, we formulate a novel idea to model the semantics of time expression as update operations. We use the update semantics-based representation to model the normalization task as predicting an operation sequence from a given natural language sequence, which is compatible with the popular Seq2Seq scheme in deep learning. We propose a distantly supervised neural parsing approach DNPTime for parsing the time expressions. DNPTime parses recognized expressions into executable operations to obtain normalized values. Specifically, DNPTime uses the intermediate results from the automatic rule generation approach ARTime to construct distant supervision samples for fine-tuning T5 models. Experimental results show that DNPTime gives the best average performances on different benchmarks with fewer human interventions.

Automatic generation of fuzzy inference systems for multivariate time series forecasting

For the time series forecasting process, the model must learn the variables' temporal dynamics to obtain consistent future values. Although its forecast may depend only on past values in some time series, there are numerous applications in which multivariate forecasting is necessary for a more accurate result. Therefore a model that deals with this type of problem is needed. In addition, explainability is an essential feature for choosing the forecast model since it is possible to extract insights and understand which variables were most important for the result generated. A Fuzzy Inference System, named AutoMFIS, is a model for multivariate time series forecasting with good accuracy and interpretability. However, despite the favorable characteristics, this system has limitations that make this technique not practical in problems with high-dimensionality databases. This article proposes an extension to the base model AutoMFIS for time series forecasting with high dimensionality data, named e-AutoMFIS. Based on the Ensemble learning theory, this new methodology applies distributed learning for automatic fuzzy rules generation and rule filtering to keep the system semantically interpretable. The proposed model is evaluated in different case studies, in which the results are compared in terms of accuracy along with other methods in the literature. In addition, in each selected problem, the interpretability aspect is also assessed.

Fuzzy coloured petri nets‐based method to analyse and verify the functionality of software

AbstractSome types of software systems, like event‐based and non‐deterministic ones, are usually specified as rules so that we can analyse the system behaviour by drawing inferences from firing the rules. However, when the fuzzy rules are used for the specification of non‐deterministic behaviour and they contain a large number of variables, they constitute a complex form that is difficult to understand and infer. A solution is to visualise the system specification with the capability of automatic rule inference. In this study, by representing a high‐level system specification, the authors visualise rule representation and firing using fuzzy coloured Petri‐nets. Already, several fuzzy Petri‐nets‐based methods have been presented, but they either do not support a large number of rules and variables or do not consider significant cases like (a) the weight of the premise's propositions in the occurrence of the rule conclusion, (b) the weight of conclusion's proposition, (c) threshold values for premise and conclusion's propositions of the rule, and (d) the certainty factor (CF) for the rule or the conclusion's proposition. By considering cases (a)–(d), a wider variety of fuzzy rules are supported. The authors applied their model to the analysis of attacks against a part of a real secure water treatment system. In another real experiment, the authors applied the model to the two scenarios from their previous work and analysed the results.

Three-stage fuzzy-metaheuristic algorithm for smart cities: Scheduling mobile charging and automatic rule tuning in WRSNs

Recent breakthroughs in wireless power transfer technology have been promising for empowering and enabling seamless operation of wireless sensors and developing sustainable systems such as wireless rechargeable sensor networks (WRSNs). Relying on this technology and focusing on WRSNs, this paper proposes an adaptive charging scheduling algorithm called the three-stage fuzzy metaheuristic algorithm (TSFM) by using the whale optimization algorithm (WOA) and a multiobjective function designed to meet the application requirements of WRSNs in smart cities. The TSFM algorithm benefits from the WOA for the automated configuration and optimization of the fuzzy rule-base table in a three-stage fuzzy inference system for clustering, routing, and scheduling mobile charging simultaneously. Unlike the other methods, the TSFM algorithm determines the ratio of effect of fuzzy input parameters of every stage based on application and environment in addition to considering effective parameters for every stage, especially for charging scheduling (e.g., the energy of a charge-request node, the distance between a charge-request node and a mobile charging (MC), the density of a charge-request node, the average energy consumption rate of a charge-request node, and the time elapsed after a charge request). Furthermore, for the proper distribution of energy in a network, better responsiveness, and optimal use of a charging robot, two energy thresholds are defined in the TSFM algorithm, namely as the charging request issue threshold and the charging operation completion threshold. The TSFM algorithm is adaptable to applications, environments, and requirements of WRSNs not only by using the features of nodes (as the fuzzy input parameters) but also by considering the application-specific features, environments, and infrastructure (e.g., network size, number of nodes, base station (BS) positions, and designer’s goals) within the process of optimizing tunable parameters. The TSFM algorithm was evaluated and compared with some common methods such as FCFS, NJNP, and ESS in terms of functional metrics such as energy utility, charging latency, survival rate, death rate of nodes, system stability, number of packets received by BS, number of request nodes, number of alive nodes, average residual energy, adaptability analysis, and statistical validation in two scenarios and six applications. According to the simulation results, the proposed TSFM algorithm improved the performance criteria and outperformed the other methods by far in terms of application. For instance, the TSFM algorithm improved the minimum system stability rate by 167%, 0.73%, and 123% compared with FCFS, NJNP, and ESS methods, respectively.

EnviRule: an end-to-end system for automatic extraction of reaction patterns from environmental contaminant biotransformation pathways.

Transformation products (TPs) of man-made chemicals, formed through microbially mediated transformation in the environment, can have serious adverse environmental effects, yet the analytical identification of TPs is challenging. Rule-based prediction tools are successful in predicting TPs, especially in environmental chemistry applications that typically have to rely on small datasets, by imparting the existing knowledge on enzyme-mediated biotransformation reactions. However, the rules extracted from biotransformation reaction databases usually face the issue of being over/under-generalized and are not flexible to be updated with new reactions. We developed an automatic rule extraction tool called enviRule. It clusters biotransformation reactions into different groups based on the similarities of reaction fingerprints, and then automatically extracts and generalizes rules for each reaction group in SMARTS format. It optimizes the genericity of automatic rules against the downstream TP prediction task. Models trained with automatic rules outperformed the models trained with manually curated rules by 30% in the area under curve (AUC) scores. Moreover, automatic rules can be easily updated with new reactions, highlighting enviRule's strengths for both automatic extraction of optimized reactions rules and automated updating thereof. enviRule code is freely available at https://github.com/zhangky12/enviRule.

Prediction approaches for partly missing multi‐omics covariate data: A literature review and an empirical comparison study

AbstractAs the availability of omics data has increased in the last few years, more multi‐omics data have been generated, that is, high‐dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block‐wise missing multi‐omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi‐omics data sets, we compare the predictive performances of several of these approaches for different block‐wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data

AI for identifying social norm violation

Identifying social norms and their violation is a challenge facing several projects in computational science. This paper presents a novel approach to identifying social norm violations. We used GPT-3, zero-shot classification, and automatic rule discovery to develop simple predictive models grounded in psychological knowledge. Tested on two massive datasets, the models present significant predictive performance and show that even complex social situations can be functionally analyzed through modern computational tools.

Intergenerational risk sharing in a defined contribution pension system: analysis with Bayesian optimization

AbstractWe study a fully funded, collective defined contribution (DC) pension system with multiple overlapping generations. We investigate whether the welfare of participants can be improved by intergenerational risk sharing (IRS) implemented with a realistic investment strategy (e.g., no borrowing) and without an outside entity (e.g., shareholders) that helps finance the pension fund. To implement IRS, the pension system uses an automatic adjustment rule for the indexation of individual accounts, which adapts to the notional funding ratio of the pension system. The pension system has two parameters that determine the investment strategy and the strength of the adjustment rule, which are optimized by expected utility maximization using Bayesian optimization. The volatility of the retirement benefits and that of the funding ratio are analyzed, and it is shown that the trade-off between them can be controlled by the optimal adjustment parameter to attain IRS. Compared with the optimal individual DC benchmark using the life cycle strategy, the studied pension system with IRS is shown to improve the welfare of risk-averse participants, when the financial market is volatile.

An automatic complex event processing rules generation system for the recognition of real-time IoT attack patterns

The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used.