Rule-based System Research Articles

Application and existing problems of artificial intelligence technology in the agricultural field

In recent years, the application of artificial intelligence technology in the field of agriculture has been rapidly developed. This paper summarizes the application of artificial intelligence in agriculture and divides it into two main directions: monitoring system and expert system. This paper analyzes the soil monitoring, pest monitoring, and plant growth detection of the monitoring system, the simple decision chain of the expert system, and the complex expert system combined with artificial intelligence technology. Utilizing sensor networks, image processing, and machine learning techniques, artificial intelligence enables real-time monitoring of soil parameters, automatic identification of pest and disease, analysis of plant growth status, and provision of tailored management recommendations. By employing rule-based expert systems, artificial intelligence assists farmers in making informed decisions. These applications have significantly advanced resource management optimization, pest control, precise growth monitoring, and intelligent decision-making in agriculture. At the end of the article, this paper summarizes the full text and looks forward to the future trend.

ANALYSIS OF FUZZY IMPLICATION

Fuzzy logic (fuzzy sets) possesses some widespread application in decision-making due to its ability to handle uncertainties in the implemented data and information. The application of fuzzy logic in decision-making usually involves the design of fuzzy rule-based systems. The functioning of the designed fuzzy systems can be compactly defined as the fuzzy inference process. One of the main steps of fuzzy inference is fuzzy implication, and, therefore, the selection of proper implication function possesses the great impact on the corresponding decision-making process. The currently reported research provides a comparative analysis on two fuzzy implications: Ali-2 and Zadeh. The reported research involves the application of an efficiency index and is performed on a real-world example. Keywords: Fuzzy implication, If-Then rules, membership function, fuzzy set, efficiency index

A journey from AI to Gen-AI

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Machine Translation and Natural Language Processing

Machine translation and natural language processing are powerful tools that allow for the automated translation of text from one language to another. Machine translation systems have evolved from rule-based systems to statistical and neural network-based approaches, with each approach having its own strengths and weaknesses. NLP, on the other hand, focuses on understanding the structure and meaning of natural language and includes tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Thegoalofmachinetranslation,a subfield of natural language processing (NLP), is to create platform that translate text or speechautomatically.RBMT provides linguistic accuracy and control, but linguistic resources must be built and maintained meticulously. However, SMT makes use of large-scale parallel corpora to automatically learn translation patterns, making it adaptable to new language pairs and context-dependent translations.However, SMT may struggle with handling grammatical nuances and domain-specific vocabulary. Hybrid approaches combining the best of both RBMT andSMT and newer approaches like neural machine translation (NMT) are being explored to overcome the limitations and improve the quality of translation output.

Sagicor’s digital transformation maturity journey

This teaching case documents the digitization and data analytic maturity journey of a financial conglomerate of five distinct companies. Each with its own data needs, data priorities, data silos, governance requirements, and enterprise systems. The protagonist is charged with the task of aligning the companies to a single data strategy and fostering a data-driven culture. Progress is made as evidenced by the implementation of a rule-based engine system to address fraud, a data warehouse to improve business intelligence as well as improvement in the Gartner data analytic maturity level. Despite these technical successes the journey continues as the group seeks to address data quality and governance issues, increase data literacy, entrench a data-driven culture as well as move to the next Gartner data analytic maturity level.

Towards Dynamic Fuzzy Rule Interpolation via Density-Based Rule Promotion from Interpolated Outcomes

Traditional fuzzy rule-based systems struggle with scenarios where knowledge gaps exist in the problem domain, due to limited data or experience. Fuzzy rule interpolation (FRI) effectively addresses the challenge of inference in fuzzy systems when faced with unmatched observations, due to the employment of an incomplete or sparse rule base. It generates temporary, interpolated rules for the unmatched observations, ensuring continued inference capability. However, the resultant valuable interpolated rules are conventionally discarded. This paper introduces a formal approach for dynamic fuzzy rule interpolation (D-FRI), based on the concept of density-based rule promotion and assisted by the use of the OPTICS clustering algorithm, through exploiting frequently appearing interpolated rules on the fly. This enhances the system’s knowledge coverage, efficiency, and robustness over time. An implementation of such a D-FRI system is presented, which combines transformation-based fuzzy rule interpolation (T-FRI) with OPTICS clustering. This offers an effective mechanism for evaluating and subsequently selecting potentially powerful interpolated rules for the system to dynamically enrich its knowledge base. The implemented system is verified by experimental investigations.

Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.

Rule-Based Natural Language Processing Pipeline to Detect Medication-Related Named Entities: Insights for Transfer Learning.

We document the procedure and performance of a rule-based NLP system that, using transfer learning, automatically extracts essential named entities related to drug errors from Japanese free-text incident reports. Subsequently, we used the rule-based annotated data to fine-tune a pre-trained BERT model and examined the performance of medication-related incident report prediction. The rule-based pipeline achieved a macro-F1-score of 0.81 in an internal dataset and the BERT model fine-tuned with rule-annotated data achieved a macro-F1-score of 0.97 and 0.75 for named entity recognition and relation extraction tasks, respectively. The model can be deployed to other, similar problems in medication-related clinical texts.

Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data

Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom’s retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciates the uncertainty of the data and provides insights into churn modeling. The goal of the study was to evaluate the applicability of the Mamdani and Sugeno models for building a churn model based on a limited but real-world dataset enriched with feature engineering. The additional goal was to find features most usable for churn modeling. Four metrics—accuracy, recall, precision, and F1-score—were used to estimate the performance of the models. The developed fuzzy rule-based systems show that to generalize possible churn identification factors with fuzzy rules, it is advisable to begin with features such as the change in the total amount of the invoice in the last period before the churning compared to the previous one, the total amount of the invoice in the period preceding the churning, the total amount of subscription in two months before the churning, the time of cooperation with the operator, and the number of calls out of the last quarter before leaving.

Contractual AI: Toward More Aligned, Transparent, and Robust Dialogue Agents

We present a new framework for AI alignment called Contractual AI, and apply it to the setting of dialogue agents chatting with humans. This framework incorporates and builds on previous approaches to alignment, such as Constitutional AI. We propose that fully aligned systems may need both a "think fast" and a "think slow" systems for approximating complex human judgements. Fast thinking (System 1) is computationally cheap but rigid and brittle in novel situations, while slow thinking (System 2) is more expensive but more flexible and robust. System 1 makes judgements by asking whether a rule or principle is violated. System 2 does the explicit reasoning that produces the rules, explicitly tallying costs and benefits for all stakeholders. Rule-based systems like Constitutional AI correspond roughly to System 1. Here, we implement a prototype of System 2, and lay out a road-map for enabling the system to make more thorough and accurate considerations for all stakeholder groups, including those underrepresented in the training data (e.g. racial minorities). For initial testing, we guided the decision process through the steps of: 1) identifying all stakeholders, 2) listing their individual concerns, 3) soliciting the projected opinions of various experts, and 4) combining the expert opinions into a final moral judgement. The resulting text was less generic, more aware of complex stakeholder needs, and ultimately more actionable.

Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis.

Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE. Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data. We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome. Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.

Clarifying the Dialogue-Level Performance of GPT-3.5 and GPT-4 in Task-Oriented and Non-Task-Oriented Dialogue Systems

Although large language models such as ChatGPT and GPT-4 have achieved superb performances in various natural language processing tasks, their dialogue performance is sometimes not very clear because the evaluation is often done on the utterance level where the quality of an utterance given context is the evaluation target. Our objective in this work is to conduct human evaluations of GPT-3.5 and GPT-4 to perform MultiWOZ and persona-based chat tasks in order to verify their dialogue-level performance in task-oriented and non-task-oriented dialogue systems. Our findings show that GPT-4 performs comparably with a carefully created rule-based system and has a significantly superior performance to other systems, including those based on GPT-3.5, in persona-based chat.

A novel extended rule-based system based on K-Nearest Neighbor graph

The Belief Rule-Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule-Based (EBRB) system offers a solution to this problem by using data-driven methods. However, using EBRB system requires the traversal of the entire rule base, which can be time-consuming and result in the activation of many irrelevant rules, leading to an incorrect decision. Existing search optimization methods can somewhat solve this issue, but they have limitations. Moreover, the calculation of the rule activation weight only considers the similarity between input data and a single rule, ignoring the influence of the rule linkage. To address these problems, we propose a new EBRB system based on the K-Nearest Neighbor graph index (Graph-EBRB). We introduce the Hierarchical Navigable Small World (HNSW) algorithm to create the K-Nearest Neighbor graph index of the EBRB system. This index allows us to efficiently search and activate a set of key rules. We also propose a new activation weight calculation method based on the Graph Convolution Neural Network (GCN), and we optimize the system performance using a parameter learning strategy. We conduct a comprehensive experiment on 14 commonly used public data sets, and the results show that Graph-EBRB system significantly improves the reasoning efficiency and accuracy of the EBRB system. Finally, we apply the Graph-EBRB system to tree disease identification and achieve excellent classification performance, identifying over 90% of the diseased trees on the complete dataset.

Use of an ultrasound picture archiving and communication system to answer research questions: Description of data cleaning methods.

Ultrasound picture archiving and communication system (PACS) databases are useful for quality improvement and clinical research but frequently contain free text that is not easily readable. Here, we present a method to extract and clean a semi-structured echocardiography (cardiac ultrasound) PACS database. Echocardiography studies between 1 January 2010 and 31 December 2018 were extracted using a data mining tool. Numeric variables were recoded with extreme values excluded. Analysis of free text, including descriptions of the heart valves and right and left ventricular size and function, was performed using a rule-based system. Different levels of free text variables were initially identified using commonly used phrases and then iteratively developed. Randomly selected sets of 100 studies were compared to the electronic health record to validate the data cleaning process. The data validation step was performed three times in total, with Cohen's kappa ranging between 0.88 and 1.00 for the final set of data validation across all measures. Free text cleaning of semi-structured PACS databases is possible using freely available open-source software. The accuracy of this method is high, and the resulting dataset can be linked to administrative data to answer research questions. We present a method that could be used to answer clinical questions or to develop quality improvement initiatives.

Download Behavior Analysis Based on Google Play Store Data

As mobile apps continue to grow, app stores, as the main channel for users to download apps, are becoming increasingly important for developers and platforms. Understanding users 'download behavior and accurately predicting users' preferences and needs can effectively improve the effect of the recommendation system in the application mall, improve user experience and improve the download conversion rate. However, traditional rule-based recommender systems often face the problems of data sparsity and model complexity. Therefore, it is an urgent and valuable topic to analyze user download behavior combined with machine learning technology and to provide personalized recommendations and services. This study uses the download behavior information of Google Play Store users in 2018, and use three classic machine algorithms (linear regression, random forest and SVM) to model and predict the software rating, dig deep into various factors affecting the rating, and gain deep insight into users' preferences and behavior patterns. This will provide more accurate recommendation results for the application mall, improve the application quality and popularity, and improve the user satisfaction and loyalty, and provide an important reference for optimizing the recommendation system and personalized service of the application mall.

Bridging information gaps in menopause status classification through natural language processing.

To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status. A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA). NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data. By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis. NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.

Contextual Rule-Based System for Brightness Energy Management in Buildings

The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

Paving the way for automated factory planning – applying rule-based expert systems to capacity planning

Today, numerous different software systems aid factory planners in their tasks. Nevertheless, due to their lacking interoperability and project-specific approaches, generic support for automated decision-making is still missing. Investigating the state of the art, we conclude that knowledge-based information modeling is needed for decision-making support. However, as the identified approaches of previous works propose no general automation concepts. Therefore, we define the guiding research question as how to model processual domain knowledge for automating factory planning processes. In this paper, we propose a planning assistance on rule-based expert systems. The planning assistance is composed of an ontology-based information model, a planning model consisting of individual planning functions, and a domain-specific inference engine. We implement the planning assistance with Semantic Web technologies and validate the solution using an application example from capacity planning. Thereby, we demonstrate the applicability of rule-based expert systems for automated factory planning. Finally, implications for future research are drawn for exploring further application areas and developing anticipated hybrid solution concepts.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings.

Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis. The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose. The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use. With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.

Golden Jackal Optimization with Neutrosophic Rule-Based Classification System for Enhanced Traffic Sign Detection

Traffic signs detection is a critical function of automatic driving and assisted driving is a significant part of Cooperative Intelligent Transport Systems (CITS). The drivers can obtain the data attained via automated traffic sign detection to improve the comfort and security of motor vehicle driving and regulate the behaviors of drivers. Recently, deep learning (DL) has been utilized in the fields of traffic sign detection and achieve better results. But there are two major problems in traffic sign recognition to be immediately resolved. Some false sign is always detected due to the interference caused by bad weather, and illumination variation. Some traffic signs of smaller size are increasingly complex to identify than larger size hence the smaller traffic signs go unnoticed. The objective is to achieve the accuracy and robustness of traffic sign detection for detecting smaller traffic signs in a complex environment. Thus, the study presents a Golden Jackal Optimization with Neutrosophic Rule-Based Classification System (GJO-NRCS) technique for Enhanced Traffic Sign Detection. The GJO-NRCS technique aims to detect the presence of distinct types of traffic signs. In the GJO-NRCS technique, DenseNet201 model is exploited for feature extraction process and the GJO algorithm is used for hyperparameter tuning process. For final recognition of traffic signals, the GJO-NRCS technique applies NRCS technique. The simulation values of the GJO-NRCS method can be examined using benchmark dataset. The experimental results inferred that the GJO-NRCS method reaches high efficiency than other techniques.