Rule-based System Research Articles

Intelligent agents for advanced power system protection schemes

This study explores the incorporation of intelligent agents to improve power system safety, using several computational models including machine learning, rule-based systems, neural networks, and fuzzy logic. The research assesses the effectiveness and efficiency of these agents in promptly identifying, categorizing, and responding to faults in the power system architecture using empirical analysis. The results demonstrate the higher performance of agents based on neural networks, with an average improvement in fault prediction accuracy of 38% compared to systems based on rules. Furthermore, the evaluation of power system devices demonstrates a direct relationship between greater voltage ratings and increased expenses for both installation and maintenance, underscoring their crucial importance within the system. An examination of fault severity reveals that greater severity failures have a direct and significant influence on system downtime. These problems lead to longer interruptions, which emphasizes the need of implementing effective fault management systems. Intelligent agents' actions have different costs and reaction times. Actions based on neural networks have lower average costs and shorter response times, demonstrating their cost-effectiveness and efficiency in addressing faults. The study of percentage change highlights the importance of using various kinds of intelligent agents and higher-rated devices. This research offers insights into performance differences and the consequences for optimizing protection measures. This research provides a thorough understanding of how intelligent agents may enhance power system protection. It also offers guidance for future improvements in creating power grid infrastructures that are robust, dependable, and adaptable.

Utilizing Fuzzy Logic in Precision Agriculture: Techniques for Disease Detection and Management

Utilizing Fuzzy Logic in Precision Agriculture: Techniques for Disease Detection and Management

Hybrid Actor-Action Relation Extraction: A Machine Learning Approach

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. The proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. The hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. The hybrid approach uses Named Entity Recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. The model has a 93% accuracy rate and is effective in extracting actor- action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams.

Heart Disease Diagnosis Using Decision Trees with Feature Selection Method

The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78\% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease

A method for managing metrology WIP queues in an adaptive sampling and an automated context.

This paper presents a rule-based expert system for managing work in progress (WIP) in metrology queues (awaiting quality control) during capacity degradation in the metrology area. This method complements a sampling strategy that is adapted to the metrology capacity. It considers the waiting time of each product in the queue, the relevance or redundancy of its measurement data, the capacity-crisis state when it was sampled; to remove products from a metrology queue. The results show this method saves on average two lots per run and reduces the average waiting time by up to 15 minutes per run.

On the Suitability of Fuzzy Rule-Based Classification Systems with Noisy Data

On the Suitability of Fuzzy Rule-Based Classification Systems with Noisy Data

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

A Rule-Based Bionic Energy Management System for Grid-Connected Community Microgrid Using Peer-to-Peer Trading With Rapid Settlement

APT-KGL: An Intelligent APT Detection System Based on Threat Knowledge and Heterogeneous Provenance Graph Learning

APTs (Advanced Persistent Threats) have caused serious security threats worldwide. Most existing APT detection systems are implemented based on sophisticated forensic analysis rules. However, the design of these rules requires in-depth domain knowledge and the rules lack generalization ability. On the other hand, deep learning technique could automatically create detection model from training samples with little domain knowledge. However, due to the persistence, stealth, and diversity of APT attacks, deep learning technique suffers from a series of problems including difficulties of capturing contextual information, low scalability, dynamic evolving of training samples, and scarcity of training samples. Aiming at these problems, this paper proposes APT-KGL, an intelligent APT detection system based on provenance data and graph neural networks. First, APT-KGL models the system entities and their contextual information in the provenance data by a HPG (Heterogeneous Provenance Graph), and learns a semantic vector representation for each system entity in the HPG in an offline way. Then, APT-KGL performs online APT detection by sampling a small local graph from the HPG and classifying the key system entities as malicious or benign. In addition, to conquer the difficulty of collecting training samples of APT attacks, APT-KGL creates virtual APT training samples from open threat knowledge in a semi-automatic way. We conducted a series of experiments on two provenance datasets with simulated APT attacks. The experiment results show that APT-KGL outperforms other current deep learning based models, and has competitive performance against state-of-the-art rule-based APT detection systems.

Public opinion on types of voice systems for older adults.

Public opinion may influence the adoption of technologies for older adults, yet studies on different contexts of technology for older adults is limited. In an online YouGov survey (N = 500) with text-and-image vignettes, participants gave more positive ratings of social acceptability, trust, and perceived impact on eldercare when the voice assistant ("VA" system) shown in the vignette performed a functional task (medication adherence) versus when it performed a social task (companionship). The VA received more positive sentiment comments when it appeared to use a machine learning (ML)-based dialogue system compared to when it appeared to be using a rule-based dialogue system. These results may assist designers and stakeholders select what type of voice system to develop or use with older adults.

Detection of Botnet in the loT Network

The ubiquity of Internet of Things (IoT) devices has prompted security concerns, particularly in the face of evolving botnet attacks. This paper investigates the impact of botnet attacks on IoT devices and proposes a network-based detection and prevention system employing signature and anomaly-based mechanisms. Notably, our methodology extends beyond traditional detection, focusing on proactively impeding bot creation. Leveraging a Linux-based distributed system, Security Information and Event Management (SIEM) tools, and custom rules, our approach encompasses distinct phases Preprocessing, Network Security Monitoring, Rule-based IDS System, and Analysis. Experimental results with diverse PCAP files demonstrate the efficacy of custom rules, significantly enhancing alert counts for various security aspects, including network trojan detection and privacy violations. The significant finding is the substantial increase in alert counts after the integration of custom rules, exemplified in the 1.1 GB PCAP file scenario. Network trojan detection surged from 585 to 988, emphasizing the heightened efficacy of rule-based measures. Privacy breaches and bad traffic alerts also experienced significant increments, showcasing the system’s improved sensitivity and responsiveness. This finding reinforces the pivotal role of custom rules in fortifying IoT network security comprehensively.

Research on the Application of Multi-Layer Tree-Structured Belief Rule Base System in Aviation Engine Performance Evaluation

Research on the Application of Multi-Layer Tree-Structured Belief Rule Base System in Aviation Engine Performance Evaluation

Association Rule Mining for Dynamic Error Classification in the Automotive Manufacturing Industry

Association rule mining (ARM) is a data mining technique that extracts relationships between variables in dataset. This paper presents an application of ARM for dynamic error classification in the automotive manufacturing industry. The method involves the extraction of frequent patterns from historical vehicle configuration data to build a rule-based system for error classification. The presented approach includes frequent item sets, rule mining and the estimation of conditional error probabilities. The results show that ARM is a promising approach for dynamic error classification in the automotive manufacturing industry and has potential to improve product quality.

A TIME-FREQUENCY BASED SUSPICIOUS ACTIVITY DETECTION FOR ANTI-MONEY LAUNDERING

The essential tool used by criminals to introduce the profits of their crimes into the financial system is money laundering. Financial institutions have the main obligation for identifying any questionable conduct connected to money laundering. Over 90% of the present systems at these institutions are false positives and rule-based, making them unproductive. The current rule-based systems in use are to be replaced with data science-based anti-money laundering (AML) models that focus on the temporal aspects of transaction behavior and CRM elements. Because there are hundreds of conceivable combinations of customer and account features, it is difficult to do feature engineering with a sufficient level of accuracy. In this paper, we develop a new feature set based on time-frequency analysis to enhance the detection performance of suspicious transaction monitoring systems for AML systems. This feature set leverages 2-D representations of financial transactions. As a machine learning technique, random forest is used, while simulated annealing is used for hyperparameter tweaking. The effectiveness of the developed algorithm is shown in scenarios that are realistically relevant by testing it on actual banking data. It is shown that the time-frequency characteristics may distinguish between things that are suspicious and those that are not. As a consequence, these characteristics significantly raise the current data science-based transaction monitoring systems' area under curve results (above 1%). With an F-score of 59.05%, a false positive rate of 14.9% has been attained using just time-frequency characteristics. The F-Score increases to 74.06% and the false positive rate is reduced to 11.85% when transaction and CRM elements are included.

A Next-Gen Real-Time Video Alert System with Machine Learning Sensitivity

Traditional methods of violence detection in public spaces often struggle with low accuracy, limited real-time capabilities, and an inability to handle complex spatiotemporal patterns. They lack the sophistication needed to accurately distinguish between violent and non-violent activities, and their reliance on rule-based systems hinders adaptability to diverse scenarios. Moreover, their communication channels for alerts might be slow and inefficient. Mitigating the pervasive issue of violence within public spaces demands a technologically advanced approach. Addressing this imperative, we present a novel solution encompassing a profound neural network architecture. Our method harmoniously integrates a pre-trained Darknet19 model with both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, collectively orchestrated to achieve unprecedented efficacy in violence detection and prevention. Our approach commences with the extraction of spatial intricacies, meticulously executed by leveraging the potent capabilities of the Darknet19 model. Subsequently, these extracted spatial features serve as the foundational dataset for training the CNN, which in turn captures and distills essential temporal attributes inherent to the video sequences. These temporal features are then seamlessly channeled into the LSTM component of our architecture, which adeptly discerns and categorizes video-based activities into two distinct classes: manifestations of violence and non-violent behaviors. Validation and verification of our proposed model transpire upon the Fight dataset, resulting in a suite of commendable experimental outcomes. The integration of multi-modal alert dissemination mechanisms further enhances our system's efficacy. Notably, pertinent alerts are expeditiously communicated to relevant law enforcement entities through the synergistic utilization of WhatsApp, Telegram, and e-mail applications. This technologically fortified paradigm promises a transformative leap in curbing violence within public domains, empowering law enforcement agencies with real-time, actionable insights. Moreover, the proposed systems have achieved high accuracy rates of 96%, which is higher than the accuracy achieved by other state-of-the-art models.

A STUDY AND ANALYSIS ON CHATBOTS FOR BUSINESSES USING BOTSIFY

A bot is a piece of software that executes different jobs and behaves like a human by following instructions. The acronym BOT stands for Build, Operate, Transfer. As a type of bot, chatbots can be thought of as conversational agents and have become extremely useful in the field of human-computer interaction. Advances in deep learning models, language understanding, and the availability of large datasets have led to notable progress in the development and implementation of chatbots. The article examines the essential elements and difficulties involved in developing chatbots. The study discusses a variety of chatbot training techniques, including rule-based systems and more intricate neural network architectures. Botsify is a platform for creating chatbots that also uses artificial intelligence (AI) to improve dialogue. With Botsify's revolutionary power, Chatter-Champion emerges as the prototype of conversational excellence, reaching new heights. Architecture gives board summary and basic implanting methods. The unique needs and interconnections with external systems may influence the actual implementations.

A COMPREHENSIVE SYSTEM FOR DETECTING DEEPFAKE VIDEOS AND AI-GENERATED TEXT

Artificial Intelligence (AI) has changed from simple rule-based systems to com-plex machine learning models. However, this progress has also made things complicated by introducing the problem of fake content that has spread all over digital platforms. Fake contents in text and video formats pose serious threats like dissemination of misinfor-mation to manipulating public opinion. To combat this challenge, a comprehensive detec-tion system is implemented that uses three different AI models which are Dessa for deep-fake detection, LLMDet for finding the source of text and DeepSpeech for converting speech into-text. Each model serves a vital function; whereas Dessa relies on sophisticated algorithms to differentiate between real and manipulated clips; LLMDet identifies com-puter-generated texts; while DeepSpeech precisely converts spoken words into written texts. This system also consists of a reporter module that generates JSON-formatted structured reports encompassing findings and interpretations from each detection task. The system was tested using both synthetic and natural data which include videos created by synthe-sia.io tool as well as those downloaded from online sources. Through rigorous testing, it was demonstrated that the multi-model AI is highly effective with solid performance results. This work can help researchers who study fake videos and AI-generated texts. It brings together different AI models to solve difficult problems, which could be useful in spotting and dealing with fake content.

Traduzione automatica “neurale” per il ladino della Val Badia

In this report we present a neural approach to machine translation for the Val Badia variant of Ladin. To achieve good results, neural models require a large number of exemplary translations on which they can be trained. The limited availability of such parallel data for Ladin makes it necessary to synthesise such data by using monolingual texts. We mainly use texts from the Ladin newspaper “La Usc di Ladins” as a basis for this so-called back translation. We translate these texts into Italian, using a rule-based system implemented in Apertium. Using DeepL API, we postprocess these translations and improve them, mainly at grammatical level. The resulting corpus serves as a basis for the different experiments we perform when we train models for this language pair. We train Transformer models from scratch and fine-tune pretrained models. With both methods we have achieved results that outperform the statistical and rule-based approaches to machine translation investigated so far. The models have been made available by means of a web application. Furthermore we have launched a platform for the continuous revision of the corpus to allow for the continuous improvement of the model through human post-editing.

A Fuzzy Rule-based System for Portfolio Selection using Technical Analysis

A Fuzzy Rule-based System for Portfolio Selection using Technical Analysis

Transparency in Algorithmic Decision-making: Interpretable Models for Ethical Accountability

Concerns regarding their opacity and potential ethical ramifications have been raised by the spread of algorithmic decisionmaking systems across a variety of fields. By promoting the use of interpretable machine learning models, this research addresses the critical requirement for openness and moral responsibility in these systems. Interpretable models provide a transparent and intelligible depiction of how decisions are made, as opposed to complicated black-box algorithms. Users and stakeholders need this openness in order to understand, verify, and hold accountable the decisions made by these algorithms. Furthermore, interpretability promotes fairness in algorithmic results by making it easier to detect and reduce biases. In this article, we give an overview of the difficulties brought on by algorithmic opacity, highlighting how crucial it is to solve these difficulties in a variety of settings, including those involving healthcare, banking, criminal justice, and more. From linear models to rule-based systems to surrogate models, we give a thorough analysis of interpretable machine learning techniques, highlighting their benefits and drawbacks. We suggest that incorporating interpretable models into the design and use of algorithms can result in a more responsible and moral application of AI in society, ultimately benefiting people and communities while lowering the risks connected to opaque decision-making processes.

계산적 인지과학의 관점에서 바라본 과학적 설명

This paper introduces the process of explanation in computational cognitive science, which involves elucidating why phenomena or events occur within the framework of causal relationships. It highlights the similarity between everyday explanations and scientific explanations when examined through the lens of cognitive processes, noting that scientific explanations are more systematic, rigorous, and precise.
 The three crucial stages of explanation discussed are firstly gathering information to provision the system's structure, secondly generating hypotheses to advance the explanation, and thirdly evaluating competitive hypotheses or explanations. The paper goes on to introduce the computational implementation of cognitive science frameworks for scientific explanation through deductive, probabilistic, and neural network approaches.
 In the deductive approach, the discussion revolves around Hempel's (1965, 1966) explanatory model, illustrating its simulation as a rule-based system when given laws and conditions. The probabilistic approach, primarily utilizing Bayesian networks, is shown to involve tasks such as generating hypotheses and assessing suitable hypotheses through conditional probabilities. Similarly, the neural network theoretical approach demonstrates that hypothesis generation or explanation through identifying causes can occur through the interaction of neural cells.
 Notably, one significant advantage of neural network theory is its ability to perform abductive inference or hypothesis generation not only based on linguistic expressions but also by accepting visual, emotional, and non-linguistic information as inputs and producing non-linguistic outputs. This advantage is ultimately seen as the ability to accommodate a variety of expression forms, termed as a "multi-modal mode."