Rule-based Method Research Articles

Optimizing Energy Efficiency in Smart Home Automation through Reinforcement Learning and Iot

This thesis explores an innovative approach to optimizing energy efficiency in smart home environments by leveraging reinforcement learning (RL) and Internet of Things (IoT) technologies. As global energy demand rises and concerns over environmental sustainability intensify, smart homes offer a promising solution to reduce residential energy consumption while enhancing user comfort. The study presents a comprehensive architecture integrating IoT devices with RL algorithms, allowing for real-time monitoring and intelligent energy management. Through data collected from smart sensors, RL agents continuously learn and adapt to occupant behaviors and environmental changes, making optimal decisions to minimize energy usage without compromising user comfort. A real word-based analysis demonstrates that the proposed system achieves significant energy savings compared to traditional rule-based methods. The results underscore the effectiveness of combining RL and IoT for adaptive energy management, paving the way for scalable solutions that could extend to smart cities and renewable energy systems. This research provides valuable insights into how emerging technologies can contribute to sustainable energy practices in the residential sector.

Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements.

Abstract 4136550: In-hospital cardiac arrest can be detected by Innovative smart healthcare syste

Background: Predicting in-hospital cardiac arrest (IHCA) is crucial for preventing patient fatalities. However, it remains challenging due to the irregularities in time series data across various features. Previous studies relied on traditional medical scoring systems, but their accuracy was limited as the models were overly simplistic. Recent research using machine learning methods has shown improved results. In our work, we employ deep learning and time series approaches, which are considered the most powerful models for addressing complex problems in recent years. Method/Research Design: The cohort study took place in a hospital in Taiwan from January 2016 to August 2022, involving 252,000 patients in general wards. Input data encompassed basic information, vital signs, Glasgow Coma Scale scores, medication, etc., to predict IHCA occurrences within 24 hours. Given the immediacy of IHCA events, we utilized left alignment to handle time series. Evaluation metrics included the area under the receiver operating characteristic curve (AUROC). Results: We compared three types of settings. The first was based on scoring systems, where the national early warning system (NEWS) achieved an AUROC of 0.756, and the modified early warning score (MEWS) reached 0.653. In the next setting, based on the data summary within a given time interval, the machine learning models XGBoost and deep learning model TabNet achieved AUROCs of 0.863 and 0.839, respectively. This indicates that machine learning outperforms traditional medical methods. The final setting involved time series analysis, where XGBoost and the Gate Recurrent Unit (GRU) network achieved 0.848 and 0.904, respectively, surpassing all previous settings. Conclusion: Our study demonstrates that learning-based methods outperform rule-based methods in predicting IHCA events. Machine learning algorithms offer more possibilities and have the potential to address complex problems effectively. Moreover, considering time series data rather than summarizing within fixed intervals preserves information and leads to better scores. Leveraging the time series deep learning method, GRU shows promising potential in significantly reducing the risk of IHCA events.

Enhancing High-Availability Database Systems: An AI-Driven Approach to Anomaly Detection

High-availability database systems are critical components in modern IT infrastructure, demanding robust mechanisms for ensuring continuous operation and data integrity. This article explores integrating artificial intelligence (AI) techniques into anomaly detection processes for such systems, addressing the limitations of traditional rule-based and statistical methods. We present a comprehensive analysis of machine learning and deep learning approaches, including supervised and unsupervised learning models, autoencoders, recurrent neural networks, and hybrid solutions that combine AI with conventional techniques. The article examines the challenges of implementing AI-powered anomaly detection in high-availability environments, such as scalability, real-time processing, and the balance between sensitivity and specificity. Through case studies in the financial and e-commerce sectors, we demonstrate these advanced detection methods' practical applications and benefits. Our findings indicate that AI-driven approaches significantly enhance the accuracy and efficiency of anomaly detection, leading to improved system reliability and performance. The article concludes by discussing emerging trends, including edge computing and explainable AI, and their potential impact on the future of database management and anomaly detection.

SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands

Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.

A Comprehensive Review of Sentiment Analysis: From Rule-Based Methods to Deep Learning and Future Directions

A Comprehensive Review of Sentiment Analysis: From Rule-Based Methods to Deep Learning and Future Directions

Too much of a good thing: How varying levels of automation impact user performance in a simulated intrusion detection task

Cyber analysts face a demanding task when prioritizing alerts from intrusion detection systems, balancing the challenge of numerous false positives from rule-based methods with the critical need to detect genuine cyber threats, necessitating unwavering vigilance and imposing a significant cognitive burden. In this field, there exists pressure to incorporate artificial intelligence techniques to enhance the automation of analyst workflows, yet without a clear grasp of how elevating the Level of Automation impacts the allocation of attentional and cognitive resources among analysts. This paper describes a simulated AI-assisted intrusion detection task which varies five degrees of automation as well as the sensitivity of the assistant, evaluating performance-based (e.g., accuracy, response time, sensitivity, response bias) and subjective (e.g., surveys on workload and trust) measures. Participants white-listed a series of time-sensitive alerts in a simulated Snort® environment. Our findings indicate that elevating the level of automation altered participants’ behavior, evident in their tendency to display a response bias towards rejecting hits (reduced hit rate and false alarm rate) when overriding an AI’s decision. Additionally, participants subjectively reported experiencing a decreased cognitive workload with a more precise algorithm, irrespective of any variance in their actual performance. Our findings suggest the necessity for additional research before implementing further automation into analyst workflows, as the demands of tasks evolve with escalating levels of automation.

Measuring Chinese online populist discourse: an automated semantic text analysis method

Based on conceptualizing Chinese online populism as a grassroots discourse and operationalizing it in terms of people-centrist and anti-elitist dimensions, we developed an automated semantic text analysis method for analyzing Chinese online populist discourse both quantitatively and qualitatively. First, we introduced semantic role labeling to reformulate the original text into semantic triplets. Second, we created a Chinese populism dictionary containing terms signifying “the people,” “elites,” and “popular sovereignty.” Third, we developed a rule-based method to recognize populist discourse automatically. The automated semantic text analysis method can capture a great deal of qualitative information throughout this process for further analysis. The validation test showed that the automated semantic text analysis method performed satisfactorily (accuracy > 0.9). This method not only creates a broadly applicable and reliable tool for studying Chinese online populism, but it also contributes to the literature on how populism manifests in various cultural and political contexts, expanding the possibilities of populism research globally.

Pre-trained models for linking process in data washing machine

Entity Resolution (ER) has been investigated for decades in various domains as a fundamental task in data integration and data quality. The emerging volume of heterogeneously structured data and even unstructured data challenges traditional ER methods. This research mainly focuses on the Data Washing Machine (DWM). The DWM was developed in the NSF DART Data Life Cycle and Curation research theme, which helps to detect and correct certain types of data quality errors automatically. It also performs unsupervised entity resolution to identify duplicate records. However, it uses traditional methods that are driven by algorithmic pattern rules such as Levenshtein Edit Distances and Matrix comparators. The goal of this research is to assess the replacement of rule-based methods with machine learning and deep learning methods to improve the effectiveness of the processes using 18 sample datasets. The DWM has different processes to improve data quality, and we are currently focusing on working with the scoring and linking processes. To integrate the machine model into the DWM, different pre-trained models were tested to find the one that helps to produce accurate vectors that can be used to calculate the similarity between the records. After trying different pre-trained models, distilroberta was chosen to get the embeddings, and cosine similarity metrics were later used to get the similarity scores, which helped us assess the machine learning model into DWM and gave us closer results to what the scoring matrix is giving. The model performed well and gave closer results overall, and the reason can be that it helped to pick up the important features and helped at the entity matching process.

Towards automated phenotype definition extraction using large language models

Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and inherently unscalable. Large language models offer a promising avenue for automating phenotype definition extraction but come with significant drawbacks, including reliability issues, the tendency to generate non-factual data (“hallucinations”), misleading results, and potential harm. To address these challenges, our study embarked on two key objectives: (1) defining a standard evaluation set to ensure large language models outputs are both useful and reliable and (2) evaluating various prompting approaches to extract phenotype definitions from large language models, assessing them with our established evaluation task. Our findings reveal promising results that still require human evaluation and validation for this task. However, enhanced phenotype extraction is possible, reducing the amount of time spent in literature review and evaluation.

Implementation of Fuzzy Logic Scheme for Assessment of Power Transformer Oil Deterioration Using Imprecise Information

This research aims to analyze the implementation of a fuzzy logic-based approach in improving the diagnosis of power transformer oil deterioration, which is critical for maintaining the efficient performance and operational life of transformers. Traditional diagnoses are based on strict measurements that do not account for the factors of variability and uncertainty of the actual data. In this article, we perform six different types of tests in this regard, and data have been collected during the period of 2021 to 2022 of 188 power transformer failures in the New KotLakhpat Lahore unit, whose voltage range is 132/66 kv and rating capacity is 40/50 MVA. In this case, a fuzzy logic-based scheme is developed based upon the membership function, a rule-based and defuzzification method that works with imprecision and the implementation of uncertainty in assessing the condition of transformer oils. Moisture, acidity, and a dissolved gas analysis indicator, along with other indication approaches such as interfacial tension, viscosity, and tangent delta measurement, are used to analyze the deterioration process in transformer oils. In the visual representation, oil samples with the following properties were first fuzzified: 19.9 mm2/s of viscosity, 0.453 mgKOH/g of acidity, 695 ppm of DGA, 20.8 mg/kg of moisture, 19.98 of IFT, and 4.35 × 100.14 of tangent delta. The output that was generated by software using the values entered into the parameters (HI and Age) after defuzzification is 45. Fuzzy logic serves as a concrete framework for transforming the diagnostics system and deterring the threats to the entire transformer’s health and reliability in the future. By using this technique, various faults were hypothetically and practically analyzed in a transformer to implement early detection technologies with the possibility to reduce maintenance costs and extend operational life up to 45 years. Various case studies indicate the effectiveness of fuzzy logic in comparison to traditional diagnostics.

A hybrid method for enhancing aspect sentiment classification in the french language: combining rule-based and learning methods

A hybrid method for enhancing aspect sentiment classification in the french language: combining rule-based and learning methods

Extraction of Canal Distribution Information Based on UAV Remote Sensing System and Object-Oriented Method

At present, the extraction of irrigation canal network distribution information is of great significance for developing a digital twin irrigation district. However, due to the low resolution of remote sensing images, it is difficult to effectively identify the canal networks, especially for channels with a width of less than 1 m, where recognition is insufficient. Therefore, the purpose of this study is to extract canal networks of different widths in an irrigation district in Shaanxi Province as the research area. A rule-based object-oriented classification method was employed, utilizing image data collected by the DJI Mavic 3 multispectral UAV (Unmanned Aerial Vehicle) to explore the accuracy of this method in extracting canal distribution information. Based on UAV multispectral remote sensing imagery, the segmentation parameters for the remote sensing imagery were determined using ENVI 5.6 software, with the segmentation threshold set at 60 and the merging threshold set at 80. By combining the spectral and spatial differences between the canals and other ground objects, rules for extracting canal network distribution information were established, and the information on the distribution of channels in this irrigation area was finally obtained. The experimental results showed a maximum recall rate of 91.88% and a maximum precision rate of 57.59%. The overall recall precision rates for the irrigation district were 85.74% and 55.08%, respectively. This method provides a new solution for identifying and extracting canal systems in irrigation districts, offering valuable insights for acquiring canal distribution information and providing a scientific basis for precision irrigation.

3D Building Modelling from ALS Point Clouds by Delaunay Triangulation and Graph Theories

Abstract. As an important task in 3D building reconstruction, 3D roof reconstruction attracts increasing attention. Existing methods have the issue of additional errors caused by multi-step processes, resulting in relatively high uncertainty and potentially affecting the reconstruction accuracy. Deep learning-based methods to achieve the direct extraction of roof vertices and edges for reconstructing 3D roof structures tackle the issue of additional errors while leading to another problem: the reliance on large labeled datasets for training. In this study, a fully rule-based method is proposed to achieve automatic 3D roof reconstruction. Roof vertices with their edges parallel to the x-y plane are first extracted from the point clouds, and subsequently, the left roof edges connecting vertices are inferred by using Delaunay triangulation. Finally, the 3D roof structure is reconstructed by using graph analysis based on the information of roof vertices and edges. This method simplifies the process of 3D roof reconstruction, and the experiment results demonstrate that the proposed method can effectively reconstruct 3D roof structures from point clouds.

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications.

Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the 'Scaffold Identification and Naming System' (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases.

How Does a Generative Large Language Model Perform on Domain-Specific Information Extraction?─A Comparison between GPT-4 and a Rule-Based Method on Band Gap Extraction.

The advent of generative Large Language Models (LLMs) has greatly impacted the field of Natural Language Processing. However, it is inconclusive how generative LLMs perform on domain-specific information extraction tasks. This study compares the performance of GPT-4 and a rule-based information extraction method based on ChemDataExtractor on band gap information extraction, a task that has important implications for the materials science domain. No training data is required for either method, which is desirable because there is a lack of training data in the materials science domain compared with a variety of material information that is of interest. Manual evaluation on 415 randomly selected articles showed that the GPT-4 model achieved a higher level of accuracy in extracting materials' band gap information than the rule-based method (Correctness 87.95% vs 51.08%, Partial correctness 11.33% vs 36.87%, incorrectness 0.72% vs 12.05%). Further analysis of the errors reveals the strengths and weaknesses of the GPT-4 model compared to the rule-based method. The GPT-4 model shows stronger performance in interdependency resolution and complicated material name recognition, while it also has weaknesses in hallucination, identifying band gap values, and identifying band gap types. Revised prompt based on the error analysis leads to improved accuracy for GPT-4. To the best of our knowledge, this study is the first to compare the GPT-4 model and ChemDataExtractor for the band gap extraction task. This study provides evidence to support using generative LLMs for domain-specific information extraction tasks.

Membangun Model Prediksi Churn Pelanggan yang Akurat

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

Evaluation of the urban heat island effect based on 3D modeling and planning indicators for urban planning proposals

ABSTRACT Urban planning plays an important role in the formation of urban space and microclimate. As a middle-level planning in the Chinese urban planning system, regulatory planning has a direct impact on the shaping of 3D urban space and can maximize the operability of urban planning. However, 3D space formation is not considered in the present preparation of regulatory plans, which leads to a misunderstanding of possibly formed 3D landscapes and microclimatic effects after the plan implementation. To support 3D visualization and quantitative analysis of the urban heat island effect (UHI) for regulatory planning, this study proposes a rule-based 3D modeling method to efficiently generate 3D models. The main elements of 3D models (terrain, building, road, vegetation, and land parcel) and their content were determined after analyzing the characteristics of 3D models in the regulatory plan. The 3D modeling process was constructed and combined with an UHI evaluation model through CGA programming. An index called heat island potential (HIP) was used as the UHI evaluation index and can be quantified using the correlation equations between planning indicators and HIP. The proposed method can support the planners to generate 3D models for a planning proposal, and simultaneously analyze 3D spatial effect and UHI.

TSOTSALearning at LLMs4OL Tasks A and B : Combining Rules to Large Language Model for Ontology Learning

This paper presents our contribution to the Large Language Model For Ontology Learning (LLMs4OL) challenge hosted by ISWC conference. The challenge involves extracting and classifying various ontological components from multiple datasets. The organizers of the challenge provided us with the train set and the test set. Our goal consists of determining in which conditions foundation models such as BERT can be used for ontologies learning. To achieve this goal, we conducted a series of experiments on various datasets. Initially, GPT-4 was tested on the wordnet dataset, achieving an F1-score of 0.9264. Subsequently, we performed additional experiments on the same dataset using BERT. These experiments demonstrated that by combining BERT with rule-based methods, we achieved an F1-score of 0.9938, surpassing GPT-4 and securing the first place for term typing on the Wordnet dataset.

Decoding persuasion: a survey on ML and NLP methods for the study of online persuasion

The proliferation of digital communication has profoundly transformed the landscape of persuasive discourse. Online platforms have amplified the reach and impact of persuasive techniques. However, they have also enabled the rapid spread of manipulative content, targeted propaganda, and divisive rhetoric. Consequently, a wide range of computational approaches has emerged to address the multifaceted nature of digital persuasion, to detect and mitigate its harmful practices. In light of this, the paper surveys computational methods for detecting persuasive means in digital communication, focusing on how they integrate humanistic knowledge to operationalize this construct. Additionally, special emphasis is placed on models' explainability, a pivotal aspect considering these models are used by institutions to influence societal interactions. For the analysis, two primary perspectives in persuasion are defined: linguistic and argumentative. The linguistic approach analyzes specific textual features, allowing for highly accountable algorithms based on explicit rules. The argumentative approach focuses on broader persuasive mechanisms, offering greater scalability but often resulting in less explainable models due to their complexity. This tension between model sophistication and explainability presents a key challenge in developing effective and transparent persuasion detection systems. The results highlight the spectrum of methodologies for studying persuasion, ranging from analyzing stylistic elements to detecting explicitly propagandist messages. Our findings highlight two key challenges in using these algorithms to tackle societal issues of persuasion misuse: the opacity of deep learning models and the absence of a theoretically grounded distinction between vicious and virtuous persuasion. To address these challenges, we propose integrating social sciences and humanities theories to enhance the effectiveness and ethical robustness of persuasion detection systems. This interdisciplinary approach enables a more nuanced characterization of text, facilitating the differentiation between vicious and virtuous persuasion through analysis of rhetorical, argumentative, and emotional aspects. We emphasize the potential of hybrid approaches that combine rule-based methods with deep learning techniques, as these offer a promising avenue for implementing this interdisciplinary framework. The paper concludes by outlining future challenges, including the importance of multimodal and multilingual analysis, ethical considerations in handling user-generated data and the growing challenge of distinguishing between human and AI-generated persuasive content.