Rule-based System Research Articles

Propose and implement a Rule-Based System to Predict Crop Yield Production

Accurate meteorological forecasting plays a pivotal role in agricultural decision-making, particularly in determining crop yield potential and optimizing agricultural practices. This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Preliminary findings suggest that data mining techniques, including machine learning algorithms, neural networks, and ensemble methods, offer significant potential for improving the accuracy and reliability of meteorological forecasts for crop yield prediction. In conclusion, this study underscores the potential of data mining techniques in improving meteorological forecasting for crop yield potential assessment.

Revolutionizing AI Decision-Making with Hybrid Rule-Based Systems: A Novel Framework for Transparent and Accurate Outcomes

Revolutionizing AI Decision-Making with Hybrid Rule-Based Systems: A Novel Framework for Transparent and Accurate Outcomes

Enhancing Medication Safety with ML-Enhanced Decision Support Systems: A Comparative Analysis of Prescription Error Detection

Background: Medication prescription errors are a global issue, leading to significant morbidity and mortality. Traditional rule-based Medical Decision Support Systems (MDSS) are often ineffective, generating numerous false alerts and failing to detect all potential errors. This study assesses a new anomaly detection system integrated with Electronic Health Records (EHR) to improve the accuracy and utility of medication error warnings. Methods: Anomalous prescription detection was implemented alongside an existing MDSS in a real-world inpatient setting over 18 months. The new system utilized Machine Learning (ML) combined with a rule-based MDSS to analyze historical EHR data. It aimed to identify and flag high-risk prescriptions through real-time anomaly detection. The performance of this hybrid system was compared against traditional MDSS and multicriteria query (MQ) methods. A clinical pharmacist reviewed 415 patients (3401 prescriptions) to validate the effectiveness of the system, assessing notifications for accuracy, clinical relevance, and practicality. Results: The ML-enhanced MDSS demonstrated superior performance compared to traditional systems. It achieved a 76% interception rate for prescriptions needing pharmacist review and a precision rate of 75%. The hybrid system outperformed traditional MDSS and MQ methods, with areas under the ROC and PRC curves of 0.84 and 0.79, respectively, compared to 0.66 and 0.57 for MDSS and 0.7 and 0.58 for MQ approaches. Conclusion: Integrating ML with rule-based MDSS significantly improves the detection of high-risk medication prescriptions, reducing false alerts and enhancing accuracy. This hybrid approach offers a more effective tool for identifying potential medication errors and improving patient safety in inpatient settings.

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Installing photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based systems, necessitating innovative solutions. The research proposes a deep learning-based optimal energy management system designed specifically for home micro-grids that incorporate PV systems with battery energy storage, Enhanced Long Short-Term Memory (LSTM)-Based Optimal Home Micro-Grid Energy Management (OHM-GEM). Integrating an improved type of LSTM neural network called LSTM into the energy management system improves the reliability of PV power output predictions. The dependability of PV power production forecasts is increased by including a refined version of the LSTM neural network in the energy management system. The efficiency of the OHM-GEM system in maximizing PV system integration into buildings is shown by the authors using simulated data. With considerable gains in energy efficiency, cost savings, and decreased reliance on non-renewable energy sources, the results highlight the possibility of this approach to forward sustainable energy practices.

A novel CLIPS-based medical expert system for migraine diagnosis and treatment recommendation

Migraines are classified as a neurological disorder defined by recurrent headaches with pain that ranges from mild to severe. Currently, this disorder lacks a permanent cure and definitive diagnostic test. Diagnosis instead requires an assessment of physical and psychological symptoms which differ among patients. To help in the diagnosis process, medical expert systems have been developed and validated since 1960. In this paper, we propose the Migraine Diagnosis and Treatment Expert System (MDATES), a medical expert system for migraine diagnosis and treatment recommendation. The system was designed and implemented using the C Language Integrated Production System (CLIPS) shell. MDATES is able to recognize seven symptoms, two classes of migraines (chronic and episodic), and four subtypes of migraine-classification knowledge (hormonal, aura, hemiplegic, and cluster). A dataset of 300 anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by MDATES were compared against the known diagnoses, and a high level of accuracy was observed, with 67% of the 100 training cases were correctly diagnosed, and 100% of the 200 testing cases were correctly diagnosed. These results highlight the effectiveness and reliability of MDATES and provide valuable assistance to medical professionals in diagnosing migraines. Moreover, we present a literature review that places our proposed system within the broader context of rule-based expert systems for migraine diagnosis and treatment recommendation. This review explores the effectiveness, limitations, and challenges of these systems, and accurately places our system within the current landscape.

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

Automatic Poetic Metre Detection for Czech Verse

Metrical analysis of verse is an essential and challenging task in the research on versification consisting of analysing a poem and deciding which metre it is written in. Thanks to existing corpora, we can take advantage of data-driven approaches, which can be better suited to the specific versification problems at hand than rulebased systems. This work analyses the Czech accentual-syllabic verse and automatic metre assignment using the vast and annotated Corpus of Czech Verse. We define the problem as a sequence tagging task and approach it using a machine learning model and many different input data configurations. In comparison to this approach, we reimplement the existing data-driven system KVĚTA. Our results demonstrate that the bidirectional LSTM-CRF sequence tagging model, enhanced with syllable embeddings, significantly outperforms the existing KVĚTA system, with predictions achieving 99.61% syllable accuracy, 98.86% line accuracy, and 90.40% poem accuracy. The model also achieved competitive results with token embeddings. One of the most interesting findings is that the best results are obtained by inputting sequences representing whole poems instead of individual poem lines.

Neural Networks and Their Application in Natural Language Processing for Social Media Analysis

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.

A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.

Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules' identification. Our approach is applied on a sub-cohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain.

Design of A Property Product Recommendation System Using Association Rule Method Based on User Interaction Patterns

This work creates an association rule-based real estate product recommendation system. Personalizing property suggestions based on user behaviour optimises property searches. Data-driven insights enhance dynamic property market user experience. Association rules alter property advice. Data-driven insights and adaptability improve property search by proposing homes depending on user engagement patterns. Strong algorithms establish location, budget, and property associations, and association rule technology and user interaction patterns increase property recommendations. Personalized property discovery uses accurate and adaptive suggestions from continuous learning. Results reveal that user interaction pattern-based association rule techniques improve property suggestion accuracy and personalization. The system's tailored advice improves property market decisions, confirming its usefulness and adaptability. Insufficient user data might distort suggestions, especially for specific interests. Not enough user diversity can lower system accuracy. User data and privacy must be secured to optimize the recommendation system. Association rules and user engagement patterns can transform property recommendations. This innovative technique can improve property searches, provide personalized ideas, and help consumers make informed decisions in a competitive market.

Development of Land Suitability Assessment Applications for Sorghum, Sweet Potato and Sugarcane

Cultivation of food crops should be done on suitable land based on the results of the soil suitability assessment. The Soil Suitability Assessment Framework was initiated by the Food and Agriculture Organization (FAO). Today, smartphones have become a ubiquitous technology for solving problems in most environments, including: Assessment of soil suitability for food crops, plantations and many others. This white paper aims to address these benefits by transforming the framework into a mobile app. This solution aims to help land users conduct land valuations more effectively and efficiently. A rule-based system (RBS) algorithm is used to build the framework into a set of rules that are interconnected to draw land suitability conclusions. Regulations relate to annual rainfall, land topography, drainage, soil type, pH, flood risk, soil fertility, soil depth, etc. Only three of his food crops, sorghum, sweet potato, and sugarcane, will be evaluated in this study. Agroclimatic data governing crop suitability have evolved into generic and crop-specific criteria. An application that assesses land suitability for three food crops will be available on the Play Store for smartphones. Usability models were surveyed by 35 respondents who used the app. The user-friendliness of the app was evaluated as "very good."

Development of Land Suitability Assessment Applications for Sorghum, Sweet Potato and Sugarcane

Cultivation of food crops should be done on suitable land based on the results of the soil suitability assessment. The Soil Suitability Assessment Framework was initiated by the Food and Agriculture Organization (FAO). Today, smartphones have become a ubiquitous technology for solving problems in most environments, including: Assessment of soil suitability for food crops, plantations and many others. This white paper aims to address these benefits by transforming the framework into a mobile app. This solution aims to help land users conduct land valuations more effectively and efficiently. A rule-based system (RBS) algorithm is used to build the framework into a set of rules that are interconnected to draw land suitability conclusions. Regulations relate to annual rainfall, land topography, drainage, soil type, pH, flood risk, soil fertility, soil depth, etc. Only three of his food crops, sorghum, sweet potato, and sugarcane, will be evaluated in this study. Agroclimatic data governing crop suitability have evolved into generic and crop-specific criteria. An application that assesses land suitability for three food crops will be available on the Play Store for smartphones. Usability models were surveyed by 35 respondents who used the app. The user-friendliness of the app was evaluated as "very good."

An extended belief rule-based system with hybrid sampling strategy for imbalanced rule base

Imbalanced dataset is an important focus for classification. As the mainstream of addressing imbalanced dataset, data-level methods are trapped in facing difficultly-determined subjective parameters, and the inconsistency between new minority samples and original minority samples simultaneously. To address it, this paper develops an extended belief-rule-based (EBRB) system with hybrid sampling strategy, which is a white-box classifier. The hybrid sampling strategy is composed of an under-sampling process and an oversampling process, in which subjective parameters are not involved. The under-sampling is to identify and remove overlapping majority rules by iteratively determining an appropriate objective threshold for calculating the inconsistency degree of rule base, and to determine and remove redundant non-overlapping majority rules by using the density of non-overlapping rules in clustered groups. The oversampling is to design a differential evolution based iterative process to generate new minority rules in groups by minimizing the inconsistency of rule base. The distribution of original dataset is maintained extremely by balancing rules in clustered majority and minority groups, respectively. This EBRB system is used for the auxiliary diagnosis of thyroid nodules, and its superior performance is highlighted by the comparison with existing EBRB systems, representative data-level methods, and algorithm-level methods.

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.

Rule-Ranking-Based Approximate Knowledge Interpolation With Directional Monotonicity.

Fuzzy rule interpolation (FRI) empowers fuzzy rule-based systems (FRBSs) with the ability to infer, even when presented with a sparse rule base where no direct rules are applicable to a given observation. The core principle lies in creating an intermediate fuzzy rule-either interpolated or extrapolated-derived from rules neighboring the observation. Conventionally, the selection of these rules hinges upon distance metrics. While this approach is easy to grasp and has been instrumental in the evolution of various FRI methods, it is burdened by the necessity of extensive distance calculations. This becomes particularly cumbersome when swift responses are imperative or when dealing with large datasets. This article introduces a groundbreaking rule-ranking-based FRI method, termed RT-FRI, which overcomes the constraints of the longstanding distance-centric FRI approach. Instead of relying on distances, RT-FRI harnesses ranking scores for rules and unmatched observation. These scores are produced by amalgamating the antecedent attributes using aggregation functions, thus streamlining the rule selection procedure. Recognizing the rigid monotonicity demands of aggregation functions, a variant-DMRT-FRI-has been introduced to ensure directional monotonicity. Experimental results indicate that RT-FRI emerges as a highly efficient technique, with DMRT-FRI exemplifying a notable balance of accuracy and efficiency.

Deep learning in high-frequency trading: Conceptual challenges and solutions for real-time fraud detection

High-frequency trading (HFT) has transformed financial markets by enabling rapid execution of trades, exploiting market inefficiencies, and optimizing trading strategies. However, this speed and complexity also present significant challenges for real-time fraud detection. Deep learning, a subset of machine learning, offers promising solutions to these challenges through its ability to analyze large volumes of data and uncover intricate patterns. This review explores the conceptual challenges and solutions associated with deploying deep learning for fraud detection in HFT environments. One of the primary challenges in implementing deep learning for HFT fraud detection is the sheer volume and velocity of data. HFT systems generate vast amounts of transactional data in milliseconds, necessitating highly efficient and scalable deep learning models. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for this task due to their ability to process and analyze sequential data efficiently. However, these models require substantial computational resources and sophisticated infrastructure to operate in real time. Another significant challenge is the need for high accuracy and low latency in fraud detection. False positives can lead to unnecessary interventions, while false negatives can result in undetected fraudulent activities. Deep learning models must be fine-tuned to balance these risks, employing techniques such as hyperparameter optimization and ensemble learning to enhance their predictive capabilities. Additionally, integrating real-time anomaly detection methods can help identify suspicious activities promptly, reducing the window of opportunity for fraudsters. Data quality and integrity also pose substantial challenges. HFT environments are susceptible to noise and outliers, which can distort model predictions. Ensuring high-quality data through rigorous preprocessing and anomaly filtering is crucial for the accuracy of deep learning models. Techniques such as data augmentation and normalization can further improve model robustness. To address these challenges, a hybrid approach combining deep learning with traditional statistical methods and rule-based systems can be effective. This approach leverages the strengths of each method, providing a comprehensive fraud detection framework that is both accurate and responsive. Additionally, ongoing model retraining and adaptation to evolving fraud patterns are essential to maintain the effectiveness of the system. In conclusion, while deep learning presents significant opportunities for enhancing real-time fraud detection in high-frequency trading, it also requires addressing challenges related to data volume, computational demands, accuracy, and data quality. By employing a hybrid approach and continually refining models, financial institutions can effectively mitigate fraud risks and safeguard their trading operations.

Syntactic analysis of traditional houses in urban kampung

This research investigates a syntactical study and comparative analysis of the statistical and spatial characteristics of traditional houses in an urban kampung settlement, focusing on a selected case study built by a participatory Arabic community in Palembang, South Sumatra, Indonesia. This research aims to determine a formal rule basis for spatial configurations to reveal identical sociospatial structures based on syntactical analysis. This experiment measures spatial layout variation through space syntax analysis to provide a better understand of how the correlation between spatial configuration and sociospatial structures in traditional houses can be deconstructed. This syntactical analysis applies four distinctive procedures: a selected case study, data collection, statistical and graphical analysis, and graph analysis. The results indicate that the spatiality of all traditional houses in this kampung settlement highlights the spatial hierarchy order as a formal rule-based system, and approximately an average of 10% of this community is concerned with designing intelligible layouts. Rumah Batu and other dwellings have a similarity and closeness. The main dwelling’s function involves more steps to separate public and private functional rooms, but a functional transformation from a dwelling into a public facility creates a short distance for easy access by users. Additionally, this separation affects occupants, especially in terms of spatial distribution activities, movement flows, and other social phenomena. This approach provides practical and tangible benefits for preservation values related to buildings; this strategy may also change how buildings are perceived in other built environments.

Global warming communicative actions of publics in Türkiye: Utilizing fuzzy rule based system

The effectiveness of government policies and environmental initiatives to mitigate global warming relies heavily on public support, which is closely tied to public perception and awareness. Despite the scientific evidence communicated, the public remains reluctant to take preventive measures against global warming. The aim of the paper is to investigate the communicative actions of publics proposed as in the situational theory of problem solving to understand publics' communicative actions towards global warming. The paper utilizes a fuzzy rule-based system approach to analyze the communicative actions of publics to reveal non-linear relationships; whereas previous studies mostly used linear statistical analysis. The paper provides a deeper understanding into the interplay between problem recognition, constraint recognition, and involvement in shaping information behavior. The results show that the communicative actions of the publics are at a low-to-moderate level. The paper's interesting finding is the nonlinear effects of constraint recognition on communicative action about global warming. Contrary to the current literature, it was found out that the dominant factor that may convince public to start taking action towards global warming seems to be recognizing being constrained at a moderate level. Based on the results, it is suggested for policy makers and communication strategists to mitigate the negative outcomes of global warming by integrating environmental issues into education at all levels and collaborating with non-governmental organizations for national awareness campaigns which focus on increasing public problem recognition and involvement.

Survey on Knowledge Representation Models in Healthcare

Knowledge representation models that aim to present data in a structured and comprehensible manner have gained popularity as a research focus in the pursuit of achieving human-level intelligence. Humans possess the ability to understand, reason and interpret knowledge. They acquire knowledge through their experiences and utilize it to carry out various actions in the real world. Similarly, machines can also perform these tasks, a process known as knowledge representation and reasoning. In this survey, we present a thorough analysis of knowledge representation models and their crucial role in information management within the healthcare domain. We provide an overview of various models, including ontologies, first-order logic and rule-based systems. We classify four knowledge representation models based on their type, such as graphical, mathematical and other types. We compare these models based on four criteria: heterogeneity, interpretability, scalability and reasoning in order to determine the most suitable model that addresses healthcare challenges and achieves a high level of satisfaction.

Multi-objective optimization of virtual machine migration among cloud data centers

AbstractWorkload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.