Rule-based System Research Articles

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

Advancing Educational accessibility and experiences: A sophisticated Chatbot integration approach

The integration of chatbots into the educational sector has ushered in a new era of learning and administrative support. This review paper explores the dynamic landscape of chatbots within educational institutes, focusing on their evolution and the emergence of innovative trends. From their early days as basic information providers to their current roles as personalized learning companions, chatbots have undergone significant transformations. The study identifies key milestones in the evolution of chatbots in education, highlighting their transition from rule-based systems to intelligent learning assistants. Moreover, this review delves into the latest trends shaping the chatbot ecosystem in education. It underscores the growing emphasis on Al-powered personalization, multimodal interfaces, and integration with Learning Management Systems (LMS) to streamline administrative tasks. Chatbots are no longer confined to text-based interactions; they now support voice and video, making education more accessible and inclusive. Additionally, they are contributing to mental health support and counseling services, addressing the well-being of students. The paper will argue that chatbots have the potential to revolutionize education by providing students with personalized learning experiences, supporting students with special needs, and promoting student engagement. However, it is important to address the ethical implications of using chatbots in education, such as privacy and bias. The paper will be of interest to educators, researchers, and policymakers who are interested in learning more about the potential of chatbots to transform education. Keywords— Deep Learning, Machine learning, NLP, Voice Assistant, Linux, Artificial Intelligence, Speech Recognition, LLMs.

Rule-based versus AI-driven benefits allocation: GDPR and AIA legal implications and challenges for automation in public social security administration

ABSTRACT This article focuses on the legal implications of the growing reliance on automated systems in public administrations, using the example of social security benefits administration. It specifically addresses the deployment of automated systems for decisions on benefits eligibility within the frameworks of the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AIA). It compares how these two legal frameworks, each targeting different regulatory objects (personal data versus AI systems) and employing different protective measures, apply for two common system types: rule-based systems utilised for making fully automated decisions on eligibility, and machine learning AI systems utilised for assisting case administrators in their decision-making. It concludes on the combined impact that the GDPR and the AIA will have on each of these types of systems, as well as on differences in how these instruments determines the basic legality of utilising such systems within social security administration.

The accurate diagnosis for COVID-19 variants using nearly initial-rough sets

The rapid evolution of rough-set theory has prompted the need for enhanced methodologies in medical diagnostics, particularly regarding COVID-19 variant detection. This study introduces refined mathematical techniques based on topological structures (called nearly initial-rough sets) derived directly from initial-rough sets. Four categories of rough-set methodologies are presented, demonstrating heightened accuracy through comprehensive comparisons against existing methods. By leveraging these techniques, a rule-based classification system for COVID-19 variants is established, achieving 100 % accuracy measures through rigorous testing against real-world and computer-generated data. The implications of these advancements in medical diagnosis hold promise for future research, offering accessible and precise tools for variant identification and prediction. Using a medical application as a case study, we demonstrate superiority through comparative analyses, aligning mathematical results with medical data and showcasing the potential for broader applications beyond experts in topology. Furthermore, the study outlines an algorithm simplifying implementation, particularly in MATLAB, and suggests future explorations in medical, economic, and diverse theoretical frameworks to enhance applicability.

From interpretation to explanation: An analytical examination of deep neural network with linguistic rule-based model

The Deep Learning (DL) models stand out as one of the most popular and widely adopted machine learning techniques across various applications, owing to their capability to handle extensive datasets and extract complex features. However, despite its widespread usage, there are concerns regarding the interpretability and explainability of the DL model, especially if the applications are related to healthcare, where trust is paramount.To address this issue, we have proposed a linguistic rule-based explainable artificial intelligence (LR-XAI) model designed to elucidate the decision-making process of DL models in terms of rules. This LR-XAI model employs various local-level interpretation methods to analyze the different features learned during training, and a linguistic rule-based system is subsequently utilized to generate rules based on these interpretation scores. The proposed model aims to enhance both interpretability in terms of feature attributions score and explainability in terms of IF-THEN rules.We evaluated the proposed model on three diverse datasets related to the physics experiments, and custom datasets. Furthermore, we conducted a statistical analysis to rigorously compare the rules generated by different interpretation models. This critical step is paramount for a comprehensive and in-depth analysis of the quality of rules. The results of these evaluations provide valuable insights into the effectiveness and applicability of our proposed model in addressing the interpretability and explainability challenges associated with DL models.

Computational linguistics at the crossroads: A comprehensive review of NLP advancements

New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.

Current Output Estimation of 12 Volts Solar Power Line Source Using Fuzzy Logic-Based Modelling Technique

This study used the fuzzy logic-based system to estimate the output current of a solar panel system at specified amount of power and percentage of cloud cover in the atmosphere. This established the fuzzy rule-based system which was applied from a research study to analyze the behavior of the current output wherein the available solar panels are three 100 watts and two 50 watts making it difficult to measure the current with power of 175 watts and 350 watts. The amount of the produced current was established through the fuzzy rule-based system stated as the following: if the power is low and the atmosphere is almost cloudy then the current produced is very small; if the power is high and the atmosphere is cloudy then the current produced is small; if the power is medium and the atmosphere is partially cloudy then the current produced is average; if the power is high and the atmosphere is clear then the current produced is large. Through defuzzification the fuzzy system was implemented by computing the output value using the centroid method.

Advancements and challenges in AI-driven language technologies: From natural language processing to language acquisition

This paper explores the evolution and impact of artificial intelligence (AI) in the realm of language technologies. We trace the historical development of language models in AI, starting from the rule-based systems of the 1960s to the sophisticated neural networks of today. The current state-of-the-art technologies, particularly transformer-based models like OpenAI's GPT series, are examined for their capabilities and limitations. We delve into the role of AI in language acquisition and learning, highlighting AI-driven language teaching tools such as Duolingo and Babbel, and discuss their effectiveness and challenges. Furthermore, the paper explores the significant contributions of AI in second language acquisition research, including the development of predictive models and sophisticated learner profiles. Ethical considerations and challenges, such as data privacy and potential biases, are also addressed. We discuss advancements in natural language processing (NLP) applications like text and sentiment analysis, speech recognition and generation, and machine translation, along with their cross-linguistic challenges. The conclusion envisions future directions for AI in language technologies, emphasizing the need for multimodal inputs, efficiency, and enhanced interpretability.

Credit Card Fraud Detection: A Machine Learning Approach

The proliferation of credit card transactions in the digital era has exacerbated the threat of fraudulent activities, necessitating robust detection mechanisms. Traditional rule-based systems often fail to keep pace with the evolving nature of fraud, prompting the adoption of machine learning techniques. This research paper explores the efficacy of machine learning algorithms in detecting credit card fraud, aiming to enhance the security of financial transactions. Leveraging a comprehensive dataset comprising legitimate and fraudulent transactions, various machine learning models are trained, tested, and evaluated. Through meticulous experimentation and analysis, key insights are unearthed regarding the performance, strengths, and limitations of different algorithms in detecting fraudulent patterns. Furthermore, feature engineering techniques and ensemble learning methods are employed to improve detection accuracy and efficiency. The findings underscore the significance of adopting a holistic machine learning approach for credit card fraud detection, offering valuable insights for financial institutions and stakeholders in fortifying transaction security. Keywords: Credit card transactions, digital era, fraudulent activities, detection mechanisms, rule-based systems, credit card fraud.

Bilateral Elimination Rule-Based Finite Class Bayesian Inference System for Circular and Linear Walking Prediction.

The prediction of upcoming circular walking during linear walking is important for the usability and safety of the interaction between a lower limb assistive device and the wearer. This study aims to build a bilateral elimination rule-based finite class Bayesian inference system (BER-FC-BesIS) with the ability to predict the transition between circular walking and linear walking using inertial measurement units. Bilateral motion data of the human body were used to improve the recognition and prediction accuracy of BER-FC-BesIS. The mean predicted time of BER-FC-BesIS in predicting the left and right lower limbs' upcoming steady walking activities is 119.32 ± 9.71 ms and 113.75 ± 11.83 ms, respectively. The mean time differences between the predicted time and the real time of BER-FC-BesIS in the left and right lower limbs' prediction are 14.22 ± 3.74 ms and 13.59 ± 4.92 ms, respectively. The prediction accuracy of BER-FC-BesIS is 93.98%. Upcoming steady walking activities (e.g., linear walking and circular walking) can be accurately predicted by BER-FC-BesIS innovatively. This study could be helpful and instructional to improve the lower limb assistive devices' capabilities of walking activity prediction with emphasis on non-linear walking activities in daily living.

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019

ObjectiveFor ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team focused on certificates with a special public health interest and those for which DNNs have a low confidence index. MethodsSupervised seq-to-seq DNNs are trained on previously coded data to ICD-10 code multiple causes and underlying causes of death. The DNNs are then used to target death certificates to be sent to the coding team and to predict multiple causes and underlying causes of death for part of the certificates. Hence, the coding campaign for 2018 and 2019 combines three modes of coding and a loop of interaction between the three. FindingsIn this campaign, 62% of the certificates are automatically batch coded by the expert system, 3% by the coding team, and the remainder by DNNs. Compared to a traditional campaign that would have relied on automatic batch coding and manual coding, the present campaign reaches an accuracy of 93.4% for ICD-10 coding of the underlying cause (95.6% at the European shortlist level). Some limitations (risks of under- or overestimation) appear for certain ICD categories, with the advantage of being quantifiable. ConclusionThe combination of the three coding methods illustrates how artificial intelligence, automated and human codings are mutually enriching. Quantified limitations on some chapters of ICD codes encourage an increase in the volume of certificates sent for manual coding from 2021 onward.

MCell4 with BioNetGen: A Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface.

Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4's Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison.

DeepLens: Integrating Deep Learning for Image Captioning and Hashtag Generation

In this research, we introduce a groundbreaking methodology that leverages deep learning techniques to revolutionize the process of generating descriptive captions and hashtags for images, effectively bridging the gap between computer vision and natural language understanding. Traditional approaches to image caption generation have often relied on rudimentary techniques such as handcrafted features and rule-based systems, which inherently struggle to capture the intricate semantics of images and adapt to diverse datasets. Recognizing these limitations, our novel framework integrates state-of-the-art convolutional neural networks (CNNs) for precise image feature extraction and recurrent neural networks (RNNs), specifically employing the ResNet-50 architecture, for seamless sequence generation. Furthermore, in addition to traditional evaluation metrics such as BLEU scores and human assessments, our system, DeepLens, introduces a groundbreaking feature: automatic hashtag generation. By meticulously analyzing both the content and context of images, DeepLens autonomously generates hashtags that enrich social media content sharing and engagement, presenting a novel paradigm in the realm of image captioning. In addition to its advancements in captioning accuracy and user experience enhancement, DeepLens offers scalability and adaptability to various domains. Its robust architecture allows for seamless integration with different datasets and environments, making it versatile for a wide range of applications. Through this comprehensive approach, we aim to not only enhance the accuracy and relevance of generated captions but also elevate the overall user experience in navigating and interacting with visual content across various platforms and applications.

Regulatory Approaches Towards AI-Based Medical Device Cybersecurity: A Transatlantic Perspective

Abstract Cybersecurity of medical devices has become a concrete concern for regulators and policymakers in the European Union and United States. Following the COVID-19 pandemic, there has been an increase in cyber-attacks on critical healthcare infrastructures and their IT systems, which have suffered service disruptions and put patients’ health and safety at risk. The increase in cyberattacks on healthcare infrastructure, including medical devices, exacerbated by the growing digitalisation of healthcare services in the EU and the US, has led legislators and regulatory bodies to pay more attention to cybersecurity. Cybersecurity of AI-based medical devices requires the assessment of three areas subject to evolving regulatory approaches: medical devices, Artificial Intelligence (AI), and cybersecurity. Although they may appear distinguished in regulatory matters, the existence of AI-based medical devices and their possible cyber vulnerabilities makes clear that the three are intertwined and deserve closer attention from a regulatory point of view. Few scholars have devoted attention to AI and cybersecurity together. Even less, in our understanding, few comprehensive and EU/US comparative pieces of literature reflect on this specific issue. This paper aims to fill this gap and address the main implications of different regulatory approaches toward AI medical device cybersecurity in the EU and the US. The research stems from the assumption that regulation of medical devices in the EU has been historically inspired by regulatory trends in the US, although with the different cultural, societal, and legal traditions that made them adapt to the specificities of the territory. The paper observes that the US is a rule-based system reflecting a “command-and-control” approach, while the EU system is a principle-based one. While they share the main characteristic of being risk-regulation-based systems, their differences impact how AI-enhanced cybersecurity is regulated.

Creation of a Machine Learning-Based Automated System for the Multi-Component Fault Analysis of Industrial Machines

The efficiency and reliability of industrial machines are paramount for ensuring smooth operations and minimizing downtime in manufacturing environments. However, the complexity of these machines and the multitude of components they consist of pose significant challenges in identifying and diagnosing faults promptly. Traditional fault analysis methods often rely on manual inspection or simplistic rule-based systems, which are time-consuming, subjective, and prone to errors. In this study, we propose the development of a novel machine learning-based automated system for the multi-component fault analysis of industrial machines. Leveraging advancements in artificial intelligence and data analytics, our system aims to revolutionize fault detection and diagnosis by efficiently processing vast amounts of sensor data to identify anomalies and pinpoint potential faults across multiple components simultaneously. The proposed system comprises several key components, including data preprocessing techniques to handle noisy sensor data and extract relevant features, machine learning algorithms for fault detection and classification, and a user-friendly interface for visualization and interpretation of results. Additionally, the system will incorporate techniques for model explain ability to enhance trust and understanding of the automated diagnostic process.

Advancements in Text-to-Video Creation through AI Models: A Comprehensive Review

This review paper explores the dynamic landscape of text-to-video creation facilitated by Artificial Intelligence (AI) models. Examining the intersection of Natural Language Processing (NLP) and Computer Vision (CV), we delve into methodologies, challenges, and advancements shaping this evolving field. From traditional rule-based systems to advanced deep learning architectures like GPT and CLIP, the paper navigates through the diverse spectrum of AI models driving text-to-video synthesis. Challenges, such as context preservation and ethical considerations, are discussed, along with practical applications spanning entertainment, education, and communication. A comparative analysis evaluates the strengths and limitations of different models, offering insights for optimal usage. Looking forward, the paper explores future directions, emphasizing collaborative efforts and ethical considerations. This comprehensive review provides a valuable resource for those engaged in the intersection of NLP, CV, and AI-driven text-to-video technologies. This review delves into AI's role in text-to-video conversion, exploring methods like computer vision and natural language processing. It examines advancements, challenges, and implications in diverse sectors such as marketing, education, and entertainment.

Detection of Cyber Attacks and Network Attacks using Machine Learning Algorithms

With the escalating sophistication of cyber threats and the increasing complexity of network infrastructures, traditional rule-based intrusion detection systems (IDS) are proving inadequate in safeguarding against modern cyber attacks. Consequently, the integration of machine learning (ML) algorithms has emerged as a promising approach to fortify network security by enabling proactive detection and mitigation of cyber threats. This review paper comprehensively explores the application of ML algorithms in detecting various forms of cyber-attacks and network intrusions. The review begins by outlining the fundamental concepts of cyber attacks and network intrusions, providing context for the subsequent discussion on ML-based detection methodologies. It surveys the landscape of ML algorithms employed in cybersecurity, ranging from classical techniques like Support Vector Machines (SVM) and Random Forests to more advanced methods such as deep learning and ensemble models. Furthermore, the paper explains the diverse datasets utilized for training and evaluating ML-based intrusion detection systems, highlighting their significance in ensuring robust and generalizable models. Additionally, it examines the challenges and limitations associated with ML-driven detection, including issues of data scarcity, adversarial attacks, and model interpretability

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance

The market for domestic robots—made to perform household chore, is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labour, in contrast to industrial robots, which are frequently criticised for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in a number of minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviours. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions in order to maximize rewards. However, the goal of Deep Reinforcement Learning (DeepRL) is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks (NNs). The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning (DeepIRL)—it utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners.

RMDEASD: Integrating Rule Mining and Deep Learning for Enhanced Aspect-Based Sentiment Analysis Across Diverse Domains

The rapidly evolving landscape of Aspect-Based Sentiment Analysis (ABSA) in the realm of natural language processing necessitates innovative approaches to comprehend and interpret the intricate nature of sentiments expressed in textual data. Traditional ABSA methods have often struggled with the nuanced sentiments inherent in various textual sources, limited in their ability to adapt to domain-specific vernacular and context. This study introduces a novel approach that synergizes rule mining with advanced deep learning techniques, aiming to address these limitations and enhance the precision and contextual understanding in sentiment analysis. Our proposed model integrates rule-based systems with deep learning Transformers, a method recognized for its effectiveness in extracting structured, domain-specific insights. This integration results in a significant enhancement in the model's ability to capture nuanced sentiments, as demonstrated by an 8.5% increase in aspect-based sentiment analysis precision and an 8.3% improvement in accuracy over existing methods. The model employs a combination of techniques including Bi-LSTM, BiGRU-CRF with RNNs, and ontology mapping operations, forming a Hybrid Multi-Level Architecture that effectively captures both sequential data relationships and semantic context. Additionally, the model incorporates Cross-Domain Transfer Learning, utilizing BERT-based pre-trained models with added layers for contextual semantics, which has shown notable generalization capabilities across various domains. Furthermore, this study refines evaluation parameters, tailoring metrics such as domain-specific accuracy, recall, and precision to more accurately assess model performance in particular domains. This is especially pertinent in our analysis of Twitter datasets specific to industries like finance and healthcare. The inclusion of Temporal Dynamics and Aspect Summarization, using techniques like the VARMAx process, provides insights into the evolution of sentiments and aspects over time, an aspect crucial for longitudinal sentiment analysis. The comprehensive testing of our model on diverse spatial and temporal datasets reveals not only improved precision and accuracy in sentiment analysis but also a reduction in delay, demonstrating its efficiency and responsiveness. The study's findings indicate that our approach could significantly impact fields reliant on sentiment analysis, such as market analysis, public opinion research, and social media monitoring, providing a more nuanced and accurate understanding of consumer sentiments and trends. This paper's contribution lies in its innovative amalgamation of rule-based and deep learning techniques, tailored evaluation metrics, and its emphasis on temporal dynamics, setting a new precedent in the field of ABSA and opening avenues for further research and application in real-world scenarios.