Rule-based System Research Articles

Behavioral fingerprinting to detect ransomware in resource-constrained devices

The Internet of Things (IoT), a network of interconnected devices, has grown and gained traction over the last few years. This paradigm can impact our lives while also providing significant economic benefits. However, although resource-constrained IoT devices offer numerous advantages, they are also vulnerable to cyberattacks. As a result, ransomware severely threatens IoT devices managing sensitive and relevant information. Solutions based on Machine and Deep Learning (ML/DL) that consider behavioral data have been identified as promising. However, most detection solutions have been developed for Windows-based systems, which generally have more resources than IoT devices. As a result, these solutions are not suitable for resource-constrained components. In addition, no solution compares the pros and cons of different behavioral dimensions of resource-constrained devices. Thus, this work presents a framework that combines three different behavioral sources with supervised and unsupervised ML/DL algorithms to detect and classify heterogeneous ransomware impacting resource-constrained spectrum sensors. A pool of experiments has demonstrated the suitability of the proposed solution and compared its performance with a rule-based system. In conclusion, the usage of resources combined with local outlier factor and decision tree are the most promising combinations to detect anomalies and classify ransomware while consuming CPU, RAM, and time of devices in a reduced manner.

A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine ;earning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.

A hybrid decision support system for adaptive trading strategies: Combining a rule-based expert system with a deep reinforcement learning strategy

Stock trading strategies pose challenging applications of machine learning for significant commercial yields in the finance industry, drawing the attention of both economists and computer scientists. Until now, many researchers have proposed various methods to implement intelligent trading strategy systems that can support decisions regarding stock trading. Some studies have shown that the problem of trading strategies can be successfully addressed by applying hybrid approaches. Motivated by this, we propose a hybrid decision support system for adaptive trading strategies that combines a rule-based system with deep reinforcement learning to self-improve by learning with human expertise. This study overcomes the limitations of previous hybrid models that mainly have focused on optimizing trading decisions and improving forecasting accuracy. The proposed hybrid model combines decision-making information from a rule-based model to enable the agent of reinforcement learning to capture more trading opportunities. In addition, the investor's available balance states facilitate adaptive learning by interacting with the environment. Moreover, the proposed trading mechanism adjusts the volume size using the policy gradient algorithm's action probabilities, resulting in improved risk-adjusted returns. The proposed hybrid model has the potential to be a reliable trading system in real-world applications through its ability to adapt to different market scenarios, withstand stressful market conditions, reduce transaction costs, scale to various index funds, and extend the proposed hybrid structure. This study highlights the applicability of more advanced machine learning in financial areas, and we also suggest expanding this approach to adaptive decision-making systems in other fields.

Data science and machine learning methods for detecting credit card fraud

Credit card fraud remains a persistent challenge in the realm of financial security, necessitating innovative approaches for detection. This paper presents a comprehensive investigation into credit card fraud detection, focusing on integrating rule-based systems and machine learning methods to enhance accuracy and efficiency. The methodology encompasses data collection from a reputable source, thorough preprocessing, model development, and online execution. Performance evaluation employs a diverse array of metrics, including precision, recall, F1 score, accuracy, confusion matrix, false positive rate, learning curve, precision-recall curve, cumulative gains curve, and ROC curve. Results demonstrate a balanced trade-off between precision and recall, essential for effective fraud detection. Detailed discussions interpret these findings, offering valuable insights and avenues for future research. This research contributes to advancing fraud detection methodologies and holds promise for enhancing financial transaction security

How Terminology Affects Users' Responses to System Failures.

The objective of our research is to advance the understanding of behavioral responses to a system's error. By examining trust as a dynamic variable and drawing from attribution theory, we explain the underlying mechanism and suggest how terminology can be used to mitigate the so-called algorithm aversion. In this way, we show that the use of different terms may shape consumers' perceptions and provide guidance on how these differences can be mitigated. Previous research has interchangeably used various terms to refer to a system and results regarding trust in systems have been ambiguous. Across three studies, we examine the effect of different system terminology on consumer behavior following a system failure. Our results show that terminology crucially affects user behavior. Describing a system as "AI" (i.e., self-learning and perceived as more complex) instead of as "algorithmic" (i.e., a less complex rule-based system) leads to more favorable behavioral responses by users when a system error occurs. We suggest that in cases when a system's characteristics do not allow for it to be called "AI," users should be provided with an explanation of why the system's error occurred, and task complexity should be pointed out. We highlight the importance of terminology, as this can unintentionally impact the robustness and replicability of research findings. This research offers insights for industries utilizing AI and algorithmic systems, highlighting how strategic terminology use can shape user trust and response to errors, thereby enhancing system acceptance.

Generalizing probabilistic material implication and Bayesian conditionals

Conditional statements in natural language of the form “if A then B” have multiple interpretations that require different logical treatment. In this paper, we focus on probabilistic “if A then B” rules that are given either a Bayesian interpretation via conditional probabilities P(B|A), or couched as probabilistic material implication. While some have argued that Bayesian conditionals are the correct way to think about such rules, there are challenges with standard inferences such as modus ponens and modus tollens that might make probabilistic material implication a better candidate at times for rule-based systems employing forward-chaining; and arguably material implication is still suitable when information about prior or conditional probabilities is not available at all. We investigate a generalization of probabilistic material implication and Bayesian conditionals that combines the advantages of both formalisms in a systematic way and prove basic properties of the generalized implication, in particular, various bounds as well as properties of inference chains in graphs. And most importantly we provide a novel and natural interpretation of the generalized implication's main parameter.

Optimal rule based fuzzy-PI controller for core power control of nuclear reactor

In this paper, an optimal rule based fuzzy proportional integral controller is proposed to control the core power of the molten salt breeder reactor (MSBR) model. Firstly, a nonlinear MSBR model is considered. It is then linearized using the perturbation linearizing approach, and the transfer function of the linearized MSBR system is established. Secondly, an optimal Fuzzy-PI control structure is formulated where a PI controller is set as the principal controller for the MSBR model and a fuzzy control system is used to adjust the PI controller parameters during any disturbance in the MSBR. The rule base system of the fuzzy controller is optimized with a reshaped class topper optimization algorithm. Finally, the optimized Fuzzy-PI controller tests under a variety of conditions. Furthermore, the Nyquist stability approach is used to analyze the stability of the closed loop MSBR model.

Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Every type of machine translation system (i.e. neural, statistical, rule-based machine translation system) is equal important to build a sophistical hybrid machine translation system. Keeping this fact in my mind, I concentrate to improve statistical machine translation system with more natural way. In this paper, I try to preserve sentiment after translation to improve the overall accuracy of the machine translation system. So, I introduced senti-model here. A senti-model (sentiment model), translation model, language model, and distortion model are incorporated on the top of the beam search algorithm for decoding. At first, sentiment information is learned and modeled with translation probability by using this algorithm. Thereafter, I decode the source sentences-based on the contextual information. Overall procedure of translation modeling with a sentiment, parameter estimation for it, and senti-translation decoding (decoding with the sentiment model) are presented with empirical evidence. Experiments on a benchmark English–Hindi dataset shows that the proposed model is capable to improve the accuracy (in terms of 4.66 BLEU points, 4.09 LeBleu points, 4.67 NIST points, 5.71 RIBES points) significantly and preserves sentiment 7.79% more than the state-of-the-art technique.

Data-driven satellite monitoring method applicable to various telemetry

It is difficult to detect signs of faults for the rule-based health monitoring systems currently installed on artificial satellites in principle, and manual monitoring of satellite telemetry is conducted. However, due to lack of human resources, much of the data is left not yet well reviewed. In this study, a systematic telemetry monitoring method that screens anomalous ones applicable to various time-series telemetry is proposed. The proposed method estimates the normal range of future telemetry values by focusing on quantile statistics of each telemetry. The demonstrative application results to the real telemetry data are also reported.

Context-aware for predicting gestational diabetes using rule – based system

Nanotechnology seems to be an important scientific method for the diagnosis of a disorder that offers more precise and appropriate health information. Diabetes mellitus (DM) is a chronic condition frequently seen that severely affects human health. Gestational diabetes happens during gestation or gestational time. Gestational diabetes may arise from normal physical changes in the body experiences of a pregnant woman.. The placenta develops substances during pregnancy that conflict with just the enzyme insulin’s behavior. The Principle objective of this paper is to predict gestational diabetes using the rule-based system by creating a user profile with AbA1c. This research can be applied to a broader range of healthcare diagnostics to get better accuracy in the prediction

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

Fuzzy Rule-Based Explainer Systems for Deep Neural Networks: From Local Explainability to Global Understanding

Explainability of deep neural networks has been receiving increasing attention with regard to auditability and trustworthiness purposes. Of the various post-hoc explainability approaches, rule extraction methods assist to understand the logic that underpins their functioning. Whereas the rule-based solutions are directly managed and understood by practitioners, the use of intervals or crisp values in the antecedents that rely on numerical values might not be intuitive enough. In this case, the benefits of a linguistic representation based on fuzzy sets/rules are straightforward, as these semantically meaningful components ease the model understanding. This paper proposes fuzzy rule-based explainer systems for deep neural networks. The algorithm learns a compact yet accurate set of fuzzy rules based on features' importance (i.e., attribution values) distilled from the trained networks. These systems can be used for both local and global explainability purposes. The evaluation results of different applications revealed that the fuzzy explainers maintained the fidelity and accuracy of the original deep neural networks while implying lower complexity and better comprehensibility.

LM2L rule based adaptive filter controlled multi-functional SPV plant to enhance DG set utilization

In the existing solar photovoltaic (SPV) and diesel generator (DG) based standalone microgrid, the power factor of the DG set reaches around 0.5-0.6. Further, it encounters poor power quality performance due to harmonics currents of the local loads, coupled at the microgrid's point of interconnection (POI). All these issues significantly affect the DG set performance, causing additional losses and reducing its fuel efficiency. In addition, the SPV plant does not provide any ancillary services in the existing system and remains ideal most of the time, causing the under-utilization of microgrid's power converters. In this context, this work presents a least mean 2Lth (LM2L) rule-based adaptive filter-controlled SPV system, where its power converters are regulated to deliver multifunctional capabilities such as enhancing the DG set's power factor and power quality performance. As a result, the DG set power factor improves to unity, and the %THD value of its voltage and current reduce to below 5%. Hence, the fuel efficiency of the DG set is improved, line losses are decreased, and the additional load can be committed to the DG set capacity, which is kept reserved to supply reactive power to the load. Moreover, the modifications, which are earlier required in the DG set design to accommodate the distortion in its voltages and currents due to the nonlinear load, can be avoided. The LM2L algorithm is used as a prefilter for the measured load currents and determines their fundamental component. It has two operating rules, which decide the fundamental component's estimation depending on the system conditions. As a result, it demonstrates an improved steady state and dynamic performances compared to the existing adaptive filter algorithms. Test results verify the control and design of the microgrid on a laboratory prototype.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds

ObjectiveTo evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions. MethodsWe evaluated a novel voice-based digital assistant which audio records and processes the ICU care team’s rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system’s prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system’s prompts to a hypothetical paper checklist containing all evidence-based practices. ResultsThe positive predictive value, negative predictive value, true positive rate, and true negative rate of the system’s prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision. ConclusionA voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

Optimization of building demand flexibility using reinforcement learning and rule-based expert systems

The increasing use of renewable energy in buildings requires optimization of building demand flexibility to reduce energy costs and carbon emissions. Nevertheless, the optimization process is generally challenging that needs to consider the on-site intermittent energy supply, dynamic building energy demand, and proper utilization of energy storage systems. Leveraging the growing availability of operational data in buildings, data-driven strategies such as reinforcement learning (RL) have emerged as effective approaches to optimizing building demand flexibility. However, training a reliable RL agent is practically data-demanding and time-consuming, limiting its practical applicability. This study proposes a new strategy that integrates a rule-based expert system (RBES) and RL agents into the decision-making process to jointly reduce building energy costs, minimize the peak-to-average ratio (PAR) of grid power, and maximize PV self-consumption. In this strategy, the RBES determines system operation directly in less complex decision-making scenarios, while, in more intricate decision-making environments, it provides a reference decision for RL to explore optimal solutions further. This integration empowers RL agents to avoid unnecessary exploration and significantly enhance learning efficiency. The proposed strategy was tested using PV generation data and energy consumption data of a low energy office building. The results demonstrated an 85.7% improvement in RL learning efficiency and this strategy can successfully avoid sub-optimal convergence during policy learning. Compared to relying solely on the RBES, the proposed strategy led to 5.4% and 19.2% reductions in the electricity costs and daily PAR of grid power at peak hours, respectively. The strategy also achieved a satisfying PV self-consumption ratio of 62.4%, which is merely 0.4% lower than the optimal value determined by the RBES strategy that prioritized maximizing PV self-consumption. Additionally, compared with a model predictive control method developed for cost reduction, the strategy achieved similar cost savings while significantly reducing the decision time.

A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

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.

An assessment of the association between patient race and prior authorization program determinations in the context of radiation therapy

BackgroundWhen a physician determines that a patient needs radiation therapy (RT), they submit an RT order to a prior authorization program which assesses guideline-concordance. A rule-based clinical decision support system (CDSS) evaluates whether the order is appropriate or potentially non-indicated. If potentially non-indicated, a board-certified oncologist discusses the order with the ordering physician. After discussion, the order is authorized, modified, withdrawn, or recommended for denial. Although patient race is not captured during ordering, bias prior to and during ordering, or during the discussion, may influence outcomes. This study evaluated if associations existed between race and order determinations by the CDSS and by the overall prior authorization program. MethodsRT orders placed in 2019, pertaining to patients with Medicare Advantage health plans from one national organization, were analyzed. The association between race and prior authorization outcomes was examined for RT orders for all cancers, and then separately for breast, lung, and prostate cancers. Analyses controlled for the patient’s age, urbanicity, and the median income in the patient’s ZIP code. Adjusted analyses were conducted on unmatched and racially-matched samples. ResultsOf the 10,145 patients included in the sample, 8,061 (79.5%) were White and 2,084 (20.5%) were Black. Race was not found to have a significant association with CDSS or prior authorization outcomes in any of the analyses. ConclusionsCDSS and prior authorization outcomes suggested similar rates of clinical appropriateness of orders for patients, regardless of race. ImplicationsPrior authorization utilizing rule-based CDSS was capable of enforcing guidelines without introducing racial bias.

Building a hospitable and reliable dialogue system for android robots: a scenario-based approach with large language models

Dialogue systems implemented with android robots are expected to provide not only advanced conversational ability but also reliability and hospitality due to their human-like appearance. In this study, we aim to develop a hospitable dialogue system by encouraging open-ended utterances and responding adaptively to give users the feeling of being heard. To achieve this, utilizing large language models (LLMs) is a promising option, but task-oriented dialogue systems implemented with only LLMs often generate irrelevant, inconsistent, or contradictory utterances. Therefore, we propose a scenario-based dialogue system that subdivides the task into smaller sub-tasks, such as summarization, information extraction, and response generation, and uses LLMs in a fine-grained manner to overcome such shortcomings of the LLM-based dialogue system. Our system was evaluated in the tourist-spot recommendation task of the Dialogue Robot Competition 2022 and achieved second place in the preliminary round and first place in the final round, outperforming other rule-based dialogue systems. However, we also identified several challenges when using LLMs for android dialogue systems, including response delays due to computational complexity, hallucinations, and coordination issues between generated utterances and robot control.

Cracking the Code: Self-Explaining AI Models for Transparent Decision Making in Complex Algorithms.

This research paper explores self-explaining AI models that bridge the gap between complex black-box algorithms and human interpretability. The study focuses on techniques like LIME, SHAP, attention mechanisms, and rule-based systems to create locally interpretable models. By providing transparent and understandable explanations for AI predictions, these models enhance user trust and comprehension. Real-world applications in healthcare, finance, and autonomous systems are evaluated to demonstrate the effectiveness of self-explaining AI models. Ethical considerations regarding fairness, bias, and accountability in AI decision-making are also addressed. The findings underscore the potential of such models to unlock the mysteries of complex algorithms, making AI more accessible and interpretable for diverse applications.

Clinical Reasoning and Artificial Intelligence

Context: Artificial intelligence refers to a set of systems that are capable of performing functions similar to human intelligent functions. Today, artificial intelligence has been successfully incorporated into clinical decision support systems (CDSS). Evidence Acquisition: The current study aimed to briefly present a narrative mini-review on clinical reasoning and artificial intelligence. Data were gathered from Google Scholar, ScienceDirect, and PubMed databases using the "clinical decision support system, artificial intelligence, and clinical reasoning" keywords. Results: Clinical decision support systems are divided into two categories: Knowledge-based and data-driven. The first category is called the rule-based expert system, and the second category is also named the machine-learning system. The usefulness of the mentioned systems and artificial intelligence in interpreting algorithmic and statistical information, where the human element can easily make a mistake, is that they are much more efficient and work with fewer errors. However, when it comes to dealing with a patient and his complaints and symptoms, because of the requirement for clinical judgment, the human element works much better in obtaining a mental image of the patient’s condition. Artificial intelligence is specifically used in scenarios such as the diagnosis of electrolyte disorders, interpreting ECG findings, and recognizing the causes of myocardial hypertrophy. Nonetheless, artificial intelligence has challenges, such as a lack of responsibility for medical decisions and treatment errors. Conclusions: Referring to the above-mentioned benefits and challenges of artificial intelligence, artificial and human intelligence cannot be superior to each other, and both have an irreplaceable role in clinical decision-making. The new view is that the goal of CDSS is to help the physician make better decisions by processing vast pieces of information as a whole entity rather than individually.