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Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study's findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths. [ABSTRACT FROM AUTHOR]

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors.

In this paper a model which enables systematized generation of both conventional and novel chord structures utilizing resources of arbitrary scales according to a rule-based, computational approach is introduced. The implemented tool takes an input scale and by using its degrees, generates chords that conform to chosen constraints and decides the best fit regarding chosen criteria. The proposed model offers an unprecedented variety of precisely adjustable parameters to control the process. The design and organization of the model is explained giving the heuristics in all constraints and criteria, and examples of application as well as experiments for testing the model and its components are illustrated with related discussions. Ideas on further utilization of the model are also presented for future work.

The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures. [ABSTRACT FROM AUTHOR]

The choice of an incorrect terrain classification might lead to consequences in construction costs, design speed, or even safety. However, the current design criteria for terrain classification may be highly subjective. In Brazil, design guidelines use textual descriptors for three classes, namely level, rolling, and mountainous. This study proposes a fuzzy rule-based classifier to predict terrain classes based on average slope and slope variation. The classifier uses fuzzy logic, which can account for imprecise and vague definitions of the input variables. The classifier was built using topographic variables, i.e. slope variation and average slope, and experts' knowledge. A survey was considered to extract experts' opinions regarding different terrain classes. The classifier provided an accuracy of at least 75%, which suggests that the expert system captured the experts' perceptions of the highway classes. As a result, the proposed system can assist decision-making by providing a more consistent method for terrain classification. [ABSTRACT FROM AUTHOR]

Development in the medical field is getting fast every day. People's interest in improving their expectancy of life, their life quality, and the significant investments in medical laboratories modify the diagnosis methods, application protocols, and surgical techniques. One of the most significant milestones in the medical field have been incorporating computers to improve data analysis during the last years. Nowadays, it is a fact that computers can help physicians, i.e., the use of artificial intelligence techniques. This paper proposes a multistage prediction-based approach and a rule-based system for the treatment of abdominal aortic aneurysms. The first step is to develop a neural network model to predict 30-day mortality during and after aortic endovascular procedures. The second step aims to infer a rule-based system from the previous model. The results show that with only eight features the final predictive model can obtain an accuracy of around 87%. Furthermore, a decision tree with the same accuracy can be inferred from this model using three features and four rules [ABSTRACT FROM AUTHOR]

Eating behavior is a key factor for nutritional intake and plays a significant role in the development of eating disorders and obesity. The standard methods to detect eating behavior events (i.e., bites and chews) from video recordings rely on manual annotation, which lacks objective assessment and standardization. Yet, video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we present a rule-based system to count bites automatically from video recordings with 468 3D facial key points. We tested the performance against manual annotation in 164 videos from 15 participants. The system can count bites with 79% accuracy when annotation is available, and 71.4% when annotation is unavailable. The system showed consistent performance across varying food textures. Eating behavior researchers can use this automated and objective system to replace manual bite count annotation, provided the system’s error is acceptable for the purpose of their study. Utilizing our approach enables real-time bite counting, thereby promoting interventions for healthy eating behaviors. Future studies in this area should explore rule-based systems and machine learning methods with 3D facial key points to extend the automated analysis to other eating events while providing accuracy, interpretability, generalizability, and low computational requirements.

Several studies were conducted to demonstrate the application of Process Mining (PM) techniques to Ethereum-compatible application event data. However, the availability of event data is constrained by the application’s process awareness, which is under-reported in the literature. Based on domain analysis, which identified several challenges to mining the business process from blockchain applications, a framework was designed, instantiated, and tested in this study. The framework supports identification of appropriate cases for PM and automates the generation of event logs from blockchain data. It consists of two modules, the Process Awareness Recognizer (PAR) and the Event Log Generator (ELG). PAR is a rule-based classifier to assess the process awareness of a given application. ELG is an automated batch processing model consisting of three methods: (1) Extractor: to retrieve event data from blockchains; (2) Decoder: to transform the extracted data to a human-readable format; and (3) Formatter: to produce event log files in a format compatible with PM tools. It was validated by implementing a proof-of-concept application with an input set of 201 real-world applications. The results prove the framework’s feasibility and applicability.

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements. [ABSTRACT FROM AUTHOR]

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen's flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper. [ABSTRACT FROM AUTHOR]

In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses.

The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

The domain of the digestive system is prone to severe chronic disease in the form of liver cirrhosis, which is currently a leading cause of mortality. This article presents a new intelligent system for predicting the severity of liver fibrosis in patients with chronic viral hepatitis C. The proposed system is based on the inference capabilities of fuzzy ontology and operates on semantic rule-based techniques. A fuzzy decision tree technique was employed to generate the ontology rule base using a dataset of real fibrosis cases from the Mansoura University Hospital, Egypt. These rules were then encoded into a set of fuzzy semantic rules using the fuzzy description logic format. To evaluate the system’s effectiveness, the proposed ontology was then tested on 47 chronic HCV cases, with an attempt made to see if this correctly diagnosed the patients’ conditions. The performance of the proposed system was compared with that of the now-standard Mamdani fuzzy inference system; while the latter achieved an accuracy of 95.7/%, the proposed fuzzy ontology-based system demonstrated higher performance, with 97.8% accuracy. Furthermore, the proposed system also supports semantic interoperability between clinical decision support systems and electronic health record ecosystems. The positive impacts of this system on the correct prediction of liver fibrosis severity thus suggest that it has the potential to assist medical professionals in diagnosing and treating this dangerous disease.

Fuzzy inference systems, in general, and complex fuzzy inference systems, in particular, play an increasingly important role in many fields, such as change detection, image classification, recognition problems, etc. Despite being the well-known technique to solve with time series data, the rulebase still has the considered limitation because of the directly affecting the results as well as the processing time of these methods. To overcome this limitation, this study proposes an Adaptive spatial complex inference system that can automatically infer and adapt to the new remotely sensed image. In the proposed model, to predict the image of time t + 1, the system will generate a new rulebase according to this expected image. This new rulebase and the previous Co-Spatial-CFIS+ rulebase are evaluated using a complex fuzzy measure. This measure is built by determining the intersection domain between two rule spaces; this intersection value estimates removing, merging, or adding a newly generated rule into the current rulebase. Finally, a more suitable set of rules is obtained for image prediction. To illustrate the efficiency of the proposed approach, it is applied to the remote sensing cloud image data of the U.S. Navy. Our model evaluated the model’s effectiveness in comparison to the state-of-the-art along studies in detecting changes in remote sensing cloud images. Moreover, the findings of the experiments revealed that the proposed model could improve the change detection results in terms of $R^{2}$ , RMSE, time-consuming, and the number of rules.

Abstract Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students’ academic performance early using advanced computational techniques. Several research efforts have focused on developing systems that can predict student performance. However, there is a need for a solution that can predict student performance and identify the factors that directly influence it. This paper aims to develop a model that accurately identifies students who are at risk of low performance, while also delineating the factors that contribute to this phenomenon. The model employs explainable machine learning (ML) techniques to delineate the factors that are associated with low performance and integrates rule-based model risk flags with the developed prediction system to improve the accuracy of performance predictions. This helps low-performing students to improve their academic metrics by implementing remedial actions that address the factors of concern. The model suggests proper remedial actions by mapping the students’ performance in each identified checkpoint with the course learning outcomes (CLOs) and topics taught in the course. The list of possible actions is mapped to this checkpoint. The developed model can accurately distinguish students at risk (total grade $$< 70\%$$ < 70 % ) from students with good performance. The Area under the ROC Curve (AUC ROC) of binary classification model fed with four checkpoints reached 1.0. Proposed framework may aid the student to perform better, increase the institution’s effectiveness and improve their reputations and rankings.

Background: Clinical decision support systems (CDSS) can help identify drug-related problems (DRPs). However, the alert specificity remains variable. Defining more relevant alerts for detecting DRPs would improve CDSS. Aim: Develop electronic queries that assist pharmacists in conducting medication reviews and an assessment of the performance of this model to detect DRPs. Method: Electronic queries were set up in CDSS using "triggers" from electronic health records: drug prescriptions, laboratory values, medical problems, vital signs, demographics. They were based on a previous study where 315 patients admitted in internal medicine benefited from a multidisciplinary medication review (gold-standard) to highlight potential DRPs. Electronic queries were retrospectively tested to assess performance in detecting DRPs revealed with gold-standard. For each electronic query, sensitivity, specificity, positive and negative predictive value were computed. Results: Of 909 DRPs, 700 (77.8%) were used to create 366 electronic queries. Electronic queries correctly detected 77.1% of DRPs, median sensitivity and specificity reached 100.0% (IQRs, 100.0%–100.0%) and 99.7% (IQRs, 97.0%–100.0%); median positive predictive value and negative predictive value reached 50.0% (IQRs, 12.5%–100.0%) and 100.0% (IQRs, 100.0%–100.0%). Performances varied according to "triggers" (p < 0.001, best performance in terms of predictive positive value when exclusively involving drug prescriptions). Conclusion: Electronic queries based on electronic heath records had high sensitivity and negative predictive value and acceptable specificity and positive predictive value and may contribute to facilitate medication review. Implementing some of these electronic queries (the most effective and clinically relevant) in current practice will allow a better assessment of their impact on the efficiency of the clinical pharmacist. [ABSTRACT FROM AUTHOR]

Introduction: Autism is a nervous system disorder, and since there is no direct diagnosis for it, data mining can help diagnose the disease. Ontology as a backbone of the semantic web, a knowledge database with shareability and reusability, can be a confirmation of the correctness of disease diagnosis systems. This study aimed to provide a system for diagnosing autistic children with a combination of semantic web and data mining. Method: Data is taken from the UCI database. There were a total of 292 data records available of which 80% (234 records) were used for modeling through the decision tree. Knowledge about patients and autism disease was presented via ontology using the Protégé 5 software. The ontology has four classes and 12 properties to communicate between the individuals in the classes. The rules extracted from the decision tree were transformed into a comprehensible form (SWRL) for interpretation in the ontology using a converter. Results: Whether the child is healthy or not can be determined by the rules obtained in the decision tree. In addition, the output of the ontology using the interpretation of 25 rules confirmed the diagnosis of an Autistic child using the decision tree. The evaluation of the ontology also confirmed its correctness. Conclusion: According to the similarity between the result of the ontology and the decision tree regarding the diagnosis of the disease, the accuracy of the proposed method can be emphasized.

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%). [ABSTRACT FROM AUTHOR]

Abstract Background Adverse drug events (ADEs) can be prevented by deploying clinical decision support systems (CDSS) that directly assist physicians, via computerized order entry systems, and clinical pharmacists performing medication reviews as part of medical rounds. However, physicians using CDSS are known to be exposed to the alert-fatigue phenomenon. Our study aimed to assess the performance of PharmaCheck—a CDSS to help clinical pharmacists detect high-risk situations with the potential to lead to ADEs—and its impact on clinical pharmacists’ activities. Methods Twenty clinical rules, divided into four risk classes, were set for the daily screening of high-risk situations in the electronic health records of patients admitted to our General Internal Medicine Department. Alerts to clinical pharmacists encouraged them to telephone prescribers and suggest any necessary treatment adjustments. PharmaCheck’s performance was assessed using the intervention’s positive predictive value (PPV), which characterizes the proportion of interventions for each alert triggered. PharmaCheck’s impact was assessed by considering clinical pharmacists as a filter for ruling out futile alerts and by comparing the final clinical PPV with a pharmacist (the proportion of interventions that led to a change in the medical regimen) to the final clinical PPV without a pharmacist. Results Over 132 days, 447 alerts were triggered for 383 patients, leading to 90 interventions (overall intervention PPV = 20.1%). By risk class, intervention PPVs made up 26.9% (n = 65/242) of abnormal laboratory value alerts, 3.1% (4/127) of alerts for contraindicated medications or medications to be used with caution, 28.2% (20/71) of drug–drug interaction alerts, and 14.3% (1/7) of inadequate mode of administration alerts. Clinical PPVs reached 71.0% (64/90) when pharmacists filtered alerts and 14% (64/242) if they were not doing it. Conclusion PharmaCheck enabled clinical pharmacists to improve their traditional processes and broaden their coverage by focusing on 20 high-risk situations. Alert management by pharmacists seemed to be a more effective way of preventing risky situations and alert-fatigue than a model addressing alerts to physicians exclusively. Some fine-tuning could enhance PharmaCheck's performance by considering the information quality of triggers, the variability of clinical settings, and the fact that some prescription processes are already highly secured.

The German labor market relies heavily on vocational training, retraining, and continuing education. In order to match training seekers with training offers and to make the available data interoperable, we present a novel approach to automatically detect access to education and training in German training offers and advertisements and identify open research questions and areas for further research. In particular, we focus on (a) general education and school leaving certificates, (b) work experience, (c) previous apprenticeship, and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide technical terms and classes of the education system that are used synonymously, combining different qualifications and adding obsolete terms. Second, we provide rule-based matching to identify the need for work experience or education. However, not all qualification requirements can be matched due to incompatible data schemas or non-standardized requirements such as initial tests or interviews. Although there are several shortcomings, the presented approach shows promising results for two data sets: training and retraining advertisements.

Higher education institutions often struggle with increased dropout rates, academic underachievement, and delayed graduations. One way in which these challenges can potentially be addressed is by better leveraging the student data stored in institutional databases and online learning platforms to predict students' academic performance early using advanced computational techniques. Several research efforts have focused on developing systems that can predict student performance. However, there is a need for a solution that can predict student performance and identify the factors that directly influence it. This paper aims to develop a model that accurately identifies students who are at risk of low performance, while also delineating the factors that contribute to this phenomenon. The model employs explainable machine learning (ML) techniques to delineate the factors that are associated with low performance and integrates rule-based model risk flags with the developed prediction system to improve the accuracy of performance predictions. This helps low-performing students to improve their academic metrics by implementing remedial actions that address the factors of concern. The model suggests proper remedial actions by mapping the students' performance in each identified checkpoint with the course learning outcomes (CLOs) and topics taught in the course. The list of possible actions is mapped to this checkpoint. The developed model can accurately distinguish students at risk (total grade < 70 % ) from students with good performance. The Area under the ROC Curve (AUC ROC) of binary classification model fed with four checkpoints reached 1.0. Proposed framework may aid the student to perform better, increase the institution's effectiveness and improve their reputations and rankings. [ABSTRACT FROM AUTHOR]

In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results show that we improved the morphological analysis performance for optical character recognizer (OCR) and social media data. Thus, for the new hybrid algorithm with LSTM, the accuracy reached 99.91% for the OCR dataset and 99.82% for social media data. [ABSTRACT FROM AUTHOR]

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen’s flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper.

A system which is transparent and has less decision rules is an efficient, user‐convincing system and moreover convenient and manageable to fields like banking, business, and medical. Decision Tree (DT) is a data mining technique which is transparent and produces a set of production rules for decision‐making. However sometimes it creates some unnecessary and redundant rules which diminish its comprehensibility. Thus a system named Transparent Expert System of Rules (TESR) is proposed in this paper to efficiently improve comprehensibility of the DT by reducing the number of rules drastically without compromising accuracy. The proposed system adopts a Sequential Hill Climbing method with a flexible heuristic function to prune the insignificant rules from decision rules generated by DT. Finally, the proposed TESR system produces a transparent and comprehensible rule set for a decision. The proposed TESR performance is evaluated using 10 datasets and is compared with simple DT (ID3, C4.5, and Classification and Regression Trees) and also two of the existing transparent systems with respect to comprehensibility, accuracy, precision, recall, and F‐measures. [ABSTRACT FROM AUTHOR]

Due to the increasing implementation of cyber physical systems and the internet of things in industry, there is a trend towards flexible, multi-variant production with a very high degree of automation to strengthen the resilience of the company. Each variant is subject to high quality requirements that must be checked to ensure quality. A common means for this is quality assurance through visual inspections, which are carried out manually. Visual inspections are time-consuming yet prone to wrong decisions, since they are subjective, inconsistent, and susceptible to uncertainties. The quality of the inspection relies heavily on the experience of the personnel. This work addresses this issue through the concept and design of a system for objective decision making in visual inspection by integrating Deep Learning (DL) models with a Belief Rule Based Expert System (BRBES) inside a smart devices application. Smart devices like tablets and smartphones serve to generate information by recording and evaluating image material of the components being inspected. Based on this data, DL models are trained and used to classify defects on new image material to automate part of the inspection process. Furthermore, smart devices serve to provide context-dependent decision recommendations in the visual inspection process, which were calculated by the BRBES with the inclusion of uncertainties. The knowledge base of the BRBES is fed by the expert knowledge of experienced visual inspectors using knowledge elicitation techniques. In this way, the system can enable optimized and objectified visual inspection based on the data-driven and knowledge-driven approaches used. This paper outlines the concept of integrating DL and BRBES into a smart devices decision support system. [ABSTRACT FROM AUTHOR]

The Architecture, Engineering, Construction and Owner-operated (AECO) industry faces an array of productivity challenges, in part stemming from limited interoperability and suboptimal management of building information across project lifecycles. This paper investigates the application of rule-based systems from the manufacturing industry, especially as used in the semiconductor manufacturing sector, to improve productivity in the AECO industry. A comparative methodology is employed, examining processes in both the manufacturing and AECO industries. The results indicate that methodologies from semiconductor mask manufacturing can be effectively adapted for use in the AECO industry, particularly in design for buildability and standardized rule application. This paper includes the implementation of a Proof-of-Concept (POC) for Manufacturing Rule Check (MRC) in the AECO industry, focusing on Prefabricated Bathroom Units (PBU) production. The MRC developed for the POC could be integrated into the Model Checker platform of Singapore's CORENET X regulatory approval system, as well as deployed as a specialized application in the cloud that facilitates ease of collaboration among the design to fabrication players in the construction process. • Conducted a comparative analysis between semiconductor and AECO processes • Implemented Manufacturing Rule Check system focused on prefabricated bathrooms • Enhanced BIM and IDD interoperability with rule-based checking on regulatory platforms • Demonstrated the significance of adopting IDD to streamline AECO workflows [ABSTRACT FROM AUTHOR]

Background: Adverse drug events (ADEs) can be prevented by deploying clinical decision support systems (CDSS) that directly assist physicians, via computerized order entry systems, and clinical pharmacists performing medication reviews as part of medical rounds. However, physicians using CDSS are known to be exposed to the alert-fatigue phenomenon. Our study aimed to assess the performance of PharmaCheck-a CDSS to help clinical pharmacists detect high-risk situations with the potential to lead to ADEs-and its impact on clinical pharmacists' activities.Methods: Twenty clinical rules, divided into four risk classes, were set for the daily screening of high-risk situations in the electronic health records of patients admitted to our General Internal Medicine Department. Alerts to clinical pharmacists encouraged them to telephone prescribers and suggest any necessary treatment adjustments. PharmaCheck's performance was assessed using the intervention's positive predictive value (PPV), which characterizes the proportion of interventions for each alert triggered. PharmaCheck's impact was assessed by considering clinical pharmacists as a filter for ruling out futile alerts and by comparing the final clinical PPV with a pharmacist (the proportion of interventions that led to a change in the medical regimen) to the final clinical PPV without a pharmacist.Results: Over 132 days, 447 alerts were triggered for 383 patients, leading to 90 interventions (overall intervention PPV = 20.1%). By risk class, intervention PPVs made up 26.9% (n = 65/242) of abnormal laboratory value alerts, 3.1% (4/127) of alerts for contraindicated medications or medications to be used with caution, 28.2% (20/71) of drug-drug interaction alerts, and 14.3% (1/7) of inadequate mode of administration alerts. Clinical PPVs reached 71.0% (64/90) when pharmacists filtered alerts and 14% (64/242) if they were not doing it.Conclusion: PharmaCheck enabled clinical pharmacists to improve their traditional processes and broaden their coverage by focusing on 20 high-risk situations. Alert management by pharmacists seemed to be a more effective way of preventing risky situations and alert-fatigue than a model addressing alerts to physicians exclusively. Some fine-tuning could enhance PharmaCheck's performance by considering the information quality of triggers, the variability of clinical settings, and the fact that some prescription processes are already highly secured. [ABSTRACT FROM AUTHOR]

Smart grids are a reality in distribution systems. They have assisted in the operation, control, and most of all, the protection of urban networks, significantly solving the contingencies of these networks. This paper treats the initial stage of implementing smart grid switching devices in distribution networks. In this stage, smart grid technologies need to operate with the traditional protection elements (such as fuses, reclosers, and sectionalizers). This fact can create trouble in the protection schemes because there are two distinctive philosophies. In some companies, especially those without substantial capital, these two protection philosophies can run together for many years. The most popular intelligent electronic devices (IEDs) available in the market are studied to verify their features and the possibility to incorporate techniques to allow the two philosophies to work together. After that, the proposed approach shows how the existing IEDs can interact with the traditional devices. Special functions can also be incorporated to inform the control center of an operational problem, increasing the observability of the network. With the proposed approach, the IEDs are transformed into intelligent agents. Practical examples using real distribution systems are presented and discussed, proving the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Considerable research has demonstrated how effective requirements engineering is critical for the success of software projects. Requirements engineering has been established and recognized as one of the most important aspects of software engineering as of late. It is noteworthy to mention that requirement consistency is a critical factor in project success, and conflicts in requirements lead to waste of cost, time, and effort. A considerable number of research studies have shown the risks and problems caused by working with requirements that are in conflict with other requirements. These risks include running overtime or over budget, which may lead to project failure. At the very least, it would result in the extra expended effort. Various studies have also stated that failure in managing requirement conflicts is one of the main reasons for unsuccessful software projects due to high cost and insufficient time. Many prior research studies have proposed manual techniques to detect conflicts, whereas other research recommends automated approaches based on human analysis. Moreover, there are different resolutions for conflicting requirements. Our previous work proposed a scheme for dealing with this problem using a novel intelligent method to detect conflicts and resolve them. A rule-based system was proposed to identify conflicts in requirements, and a genetic algorithm (GA) was used to resolve conflicts. The objective of this work is to assess and evaluate the implementation of the method of minimizing the number of conflicts in the requirements. The methodology implemented comprises two different stages. The first stage, detecting conflicts using a rule-based system, demonstrated a correct result with 100% accuracy. The evaluation of using the GA to resolve and reduce conflicts in the second stage also displayed a good result and achieved the desired goal as well as the main objective of the research. [ABSTRACT FROM AUTHOR]

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general. [ABSTRACT FROM AUTHOR]

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).

The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 D'Auria, Bettini, Tognarelli, Calvanese and Menciassi.)