Classification Rules Research Articles

Distribution rule for allocation problems with priority agents using the least square method

We use the least-squares method for inconsistent systems to find a new allocation rule for rationing and surplus problems. In particular, we study allocation problems considering different priorities to satisfy the agents’ demands, which influence how the distribution is carried out. The new distribution rule is proposed by choosing different inner products defined in linear algebra and providing explicit formulas for assigning resources to the agents. Moreover, we illustrate how it can recover different allocation rules by adequately defining the priorities. As an application of the rationing problem with priority agents, we consider real data for allocating police officers in the states of Mexico. In this example, the agents represent the states of Mexico, and their priorities were established based on the criminal incidence. Also, we compute and compare the resource distribution given by classic allocation rules and the percentage of loss obtained with each one.

Design and Implementation of Teaching Quality Assessment System for Universities Based on Data Mining Algorithms

A teaching quality assessment system for universities based on data mining algorithms utilizes advanced analytical techniques to evaluate and enhance the effectiveness of teaching practices. By leveraging data mining algorithms, such as clustering, classification, and association rule mining, this system can analyze various educational data sources, including student performance metrics, course evaluations, and instructor feedback. This paper introduces Generative Pre-trained Data Analytics (GPDA) as a novel approach for assessing teaching quality in educational settings. GPDA leverages advanced data analytics techniques to predict teaching quality metrics, classify teaching quality data, and forecast various aspects of teaching effectiveness. Through comprehensive analysis and evaluation, GPDA demonstrates its accuracy, reliability, and versatility in capturing the complexities of teaching quality across diverse educational contexts. Through comprehensive analysis and evaluation, GPDA achieves a Root Mean Squared Error (RMSE) ranging from 0.152 to 0.190 and R-squared (R²) values ranging from 0.698 to 0.805, showcasing its accuracy and reliability in predicting teaching quality metrics across diverse educational contexts. Furthermore, strong positive correlations between the synthetic data generated by GPDA and real teaching quality data, with correlation coefficient values ranging from 0.87 to 0.92, validate the fidelity of GPDA-generated synthetic data.

An enhanced crack segmentation method using implicit classification and inference rules in steel bridge

An enhanced crack segmentation method using implicit classification and inference rules in steel bridge

Explainable Anomaly Detection of Synthetic Medical IoT Traffic Using Machine Learning

In the context of the Internet of Things (IoT), particularly within medical facilities, the detection and categorization of Internet traffic remain significant challenges. While conventional methods for IoT traffic analysis can be applied, obtaining suitable medical traffic data is challenging due to the stringent privacy constraints associated with the health domain. To address this, this study proposes a network traffic simulation approach using an open-source tool called IoT Flock, which supports both CoAP and MQTT protocols. The tool is used to create a synthetic dataset, to simulate IoT traffic originating from various smart devices in different hospital rooms. The study shows a complete anomaly detection analysis of IoT-Flock-generated traffic, both normal and malicious, by leveraging and comparing traditional machine learning techniques, deep learning models with multiple hidden layers, and explainable artificial intelligence techniques. The results are very promising. For the binary classification, for example, the obtained accuracy is close to 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} in the case of the CoAP protocol. Good results are also obtained when the multinomial classification is performed, observing that CoAP packets are classified better than MQTT packets, even if the identification of the different MQTT packets reaches very high metrics for the most of the considered algorithms. Moreover, the obtained classification rules are also meaningful in the considered IoT context. The results indicate that IoT-Flock synthetic data can effectively be used to train and test machine and deep learning models for detecting abnormal IoT traffic in medical scenarios. This research attempts also to bridge the gap between IoT security and healthcare, providing useful insights into securing medical IoT networks in general.

Classification of Diabetes Mellitus Sufferers Eating Patterns Using K-Nearest Neighbors, Naïve Bayes and Decission Tree

The study investigates three classification algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, and Decision Tree, for the classification of Diabetes Mellitus using a dataset from Kaggle. K-NN relies on distance calculations between test and training data, using the Euclidean distance formula. The choice of k, representing the nearest neighbor, significantly influences K-NN's effectiveness. Naïve Bayes, a probabilistic method, predicts class probabilities based on past events, and it employs the Gaussian distribution method for continuous data. Decision Trees, form prediction models with easily implementable rules. Data collection involves obtaining a Diabetes Mellitus dataset with eight attributes. Data preprocessing includes cleaning and normalization to minimize inconsistencies and incomplete data. The classification algorithms are applied using the Rapidminer tool, and the results are compared for accuracy. Naïve Bayes yields 77.34% accuracy, K-NN performance depends on the chosen k value, and Decision Trees generate rules for classification. The study provides insights into the strengths and weaknesses of each algorithm for diabetes classification

Bivariate scoring rules: Unifying the characterizations of positional scoring rules and Kemeny's rule

This paper studies social preference functions (SPFs), which map the voters' ordinal preferences over a set of alternatives to a non-empty set of strict rankings over the alternatives. Maybe the most prominent SPFs are positional scoring rules and Kemeny's rule. While these two types of rules behave intuitively quite differently, they are axiomatically surprisingly similar and we thus provide a joint axiomatic characterization of these SPFs. To this end, we introduce the class of bivariate scoring rules which generalize Kemeny's rule by weighting comparisons between alternatives depending on their positions in the voters' preference relations. In particular, this class contains both Kemeny's rule and all positional scoring rules. As our main result, we then characterize the set of bivariate scoring rules as the only SPFs that satisfy—aside from some standard axioms—mild consistency conditions for variable electorates and variable agendas. As corollaries of this result, we also derive variants of the well-known characterizations of positional scoring rules by Smith (1973) and of Kemeny's rule by Young (1988). Thus, our result unifies the independent streams of research on positional scoring rules and Kemeny's rule by giving a joint axiomatic basis of these SPFs.

Socioeconomic agents as active matter in nonequilibrium Sakoda-Schelling models.

How robust are socioeconomic agent-based models with respect to the details of the agents' decision rule? We tackle this question by considering an occupation model in the spirit of the Sakoda-Schelling model, historically introduced to shed light on segregation dynamics among human groups. For a large class of utility functions and decision rules, we pinpoint the nonequilibrium nature of the agent dynamics, while recovering an equilibrium-like phase separation phenomenology. Within the mean-field approximation we show how the model can be mapped, to some extent, onto an active matter field description. Finally, we consider nonreciprocal interactions between two populations and show how they can lead to nonsteady macroscopic behavior. We believe our approach provides a unifying framework to further study geography-dependent agent-based models, notably paving the way for joint consideration of population and price dynamics within a field theoretic approach.

Research on the Application of Improved Apriori Algorithm in Sport-Adaptive Online Teaching System Under Big Data Environment

Each student in an adaptive education system has significant differences in knowledge background, ability level and cognitive style. Therefore, to build an adaptive teaching system, it is necessary to establish an operable, reasonable and individualized student model by clarifying students’ abilities and differences. The improved Apriori algorithm under big data is the most classic association rule algorithm, which is generated by a set of candidates, and it uses the iterative method of hierarchical search to traverse a set of frequency items in the transaction database. After finding the set of frequency items, select the association according to the trust rules. This paper studies how to apply the improved Apriori algorithm to an adaptive online education system in a big data environment. An evolutionary algebra is taken with mean fit of 80, population size of 20, mean fit of 0.28, population size of 60, mean fit of 0.26, population size of 80, mean fit of 0.25, mean population size of 0.25. The population size is 200, and the average fitting is 0.24. The larger the error, the smaller the error between each indicator of the test paper and the corresponding value specified by the user. The improved Apriori algorithm in the big data environment has designed five themes of rule mining, which are mainly used for class management: class linkage, class category linkage, student basic information linkage, lecture and basic information linkage, and lecture mode linkage. They play the role of teaching assistants with a specific role.

A vector-based coastline shape classification approach using sequential deep learning model

Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential requirement for coastal analysis. With the development of Coastal Management Systems (CMS), structured and high-resolution vector-format coastlines have become increasingly available compared to remote sensing image coastlines. However, due to the challenges of accurate description and ambiguous classification rules, automatic classification of vector coastlines has been a difficult but urgent problem to solve. In this paper, we propose a data-driven approach for classifying the shape of vector coastlines, according to their morphological characteristics. The method utilizes a sequence-based deep learning algorithm to model and classify coastline segments. We construct a dataset including five representative types of vector coastlines, train and evaluate the model using this dataset. The evaluation results show that the proposed method outperforms all baselines, achieving a classification accuracy of 93.20%. This method can be integrated into existing Coastal Management Systems to enhance their morphological analysis functions, making a valuable contribution to the applications of Artificial Intelligence (AI) in coastal management.

THE LEGAL CAPACITY (AL-AHLIYYAH) OF ARTIFICIAL INTELLIGENCE FROM AN ISLAMIC JURISPRUDENTIAL PERSPECTIVE

Capacity is the legal right and empowerment of a legal person to individual responsibility. A legal person has the right to social, economic, and political duties and responsibilities in the society to sue and be sued in the law court. However, the issue of artificial persons has been gaining attention in recent times, especially its legal capacity. This paper examines the legal capacity of Artificial Intelligence from Islamic jurisprudential perspective. For instance, legal and juristic issues remain around the legal capacity of humanoids like Sophia Robot which was granted the first full citizenship in Saudi Arabia. Does that citizenship translate to full rights and responsibility like a normal human? The study uses a qualitative method to employ the doctrinal approach of analyzing Islamic jurisprudential opinions on the legal personality of an artificial person. Although there are Islamic thresholds on minors and other interdicted persons, however, the study explores the extent of interpolating classical Islamic rulings of the legal capacity of certain corporations, entities, and interdicted persons on the artificial intelligence robot. The study found that an artificial intelligence robot does not have the complete traits to be considered for natural legal capacity. However, AI has the trait of artificial personality that is justified in Islamic jurisprudence. The vicarious tortious liability can be interpolated on the artificial personality of AI to ensure the protection of the public interest. Therefore, this study lays the groundwork for further studies in understanding how Islamic law can address the rights, responsibilities, and ethical considerations surrounding the use of AI, thereby facilitating the development of comprehensive, and religiously sensitive regulatory frameworks.

Validity of inpatient electronic health record-based measures of oxygen-related therapy in the United States: Lessons applicable for studying COVID-19 endpoints.

During the COVID-19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID-19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID-19 hospitalization in administrative data versus nursing documentation. We identified 319 553 adult hospitalizations with a COVID-19 diagnosis, February 2020-October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi-structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID-19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses. In nursing documentation, the prevalence of MV and O2 supplementation among COVID-19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%). In comparison to nursing documentation, administrative data under-ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID-19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID-19 or other respiratory illnesses.

Analysis of Disciplinary Characteristics of Elementary School Students: A Survey Research

This research aims to analyze the level of student discipline character in elementary schools at the Aceh Besar Regency level. This research uses a quantitative approach with a survey research type. The population of this study were elementary school students at the Aceh Besar Regency level with a research sample of 90 students from four different schools. The sampling technique used in this research was Cluster random sampling. This research data was collected using a questionnaire. The data obtained is quantitative data which was analyzed using descriptive statistical techniques. The results of this research show that students' disciplinary attitudes are fairly good, where the strategy carried out by the school to improve the formation of disciplinary character is the habit of activities that form disciplinary character. The habits include time discipline and how to dress. The implementation of school rules and regulations in improving the disciplinary character of elementary school students in Aceh Besar Regency has instilled a disciplinary character through religious activities and activities related to disciplinary character such as haircut rules, time discipline, Duha prayers. All these activities are carried out by the school through the habituation method. Student discipline in school grows because teachers provide motivation, create class rules, and reward students who are always on time.

Implementation Of Educational Discipline In Synergy Between Parent and Teacher In Schooling In Babussalam Lombok East SMK

The purpose of this research is to further describe how the implementation of the character education discipline students. The type of research used is a qualitative approach that uses descriptive methods, data collection techniques that are observations, interviews and documentation. Primary data sources are obtained from observations and interviews with teachers, head of school and some parents of pupils, and primary data resources are derived from previous research journals that investigate disciplinary character education. The results of the research showed that there are four ways that schools are trying to support student character education, namely, performing character education programming at school, setting up school rules and class rules, monitoring student disciplinary behavior at home Through the recording of daily activities and the involvement of parents in supporting the character education programme of this discipline by the school.

Patrol Boat Strengthening Against a Collision with COLL Notation Based on Class Rules and Regulation in Indonesia – An Overview

<span lang="EN-US">Indonesian maritime security and law enforcement rely frequently on Patrol boats. However, collisions can occur during the operation, leading to potential loss of life, damage to the ship, and environmental harm. In preventing such incidents, the government needs to strengthen the patrol boats against collisions in accordance with class rules and regulations in Indonesia.</span><span lang="EN-US">The COLL notation is an additional notation for vessel collision protection, which specifies the required strength of the vessel's hull and structural components to minimize the risk of damage and reduce the consequences of a collision. This study highlights the key areas that require strengthening, including the vessel’s bow, stern, and hull, as well as the propulsion system that needs to be considered in the design stage. The addition of collision bulkheads, increasing the hull plating thickness, and reinforcing the engine mounts and shafting are also necessary to ensure the vessel's safety against collision. In conclusion, strengthening patrol boats against a collision with COLL notation based on class rules and regulations in Indonesia is one of the methods available that can be applied for the design stage to increase the level of operational safety of patrol boats.</span>

Training neural networks with classification rules for incorporating domain knowledge

Neural networks are capable of learning complex concepts and tasks, given abundant training data. In real-world applications where data collection can be difficult, integrating domain knowledge into the model can reduce the burden on data requirements and allow human experts greater control over model decisions. This paper focuses on incorporating conditional statements for tabular data as classification rules, which have a simple structure and are easy to construct. We introduce a general rule loss constraint to guide neural network training in a model agnostic manner, and propose confidence learning to automatically weigh the contribution of multiple candidate rules. Experimental evaluation with three real-world datasets shows that the rule loss can substantially increase model performance, particularly when training data is limited.

Optimal control under uncertainty: Application to the issue of CAT bonds

We propose a general framework for studying optimal issue of CAT bonds in the presence of uncertainty on the parameters. In particular, the intensity of arrival of natural disasters is inhomogeneous and may depend on unknown parameters. Given a prior on the distribution of the unknown parameters, we explain how it should evolve according to the classical Bayes rule. Taking these progressive prior-adjustments into account, we characterize the optimal policy through a quasi-variational parabolic equation, which can be solved numerically. We provide examples of application in the context of hurricanes in Florida.

Refined Characterizations of Approval-Based Committee Scoring Rules

In approval-based committee (ABC) elections, the goal is to select a fixed-size subset of the candidates, a so-called committee, based on the voters' approval ballots over the candidates. One of the most popular classes of ABC voting rules are ABC scoring rules, for which voters give points to each committee and the committees with maximal total points are chosen. While the set of ABC scoring rules has recently been characterized in a model where the output is a ranking of all committees, no full characterization of these rules exists in the standard model where a set of winning committees is returned. We address this issue by characterizing two important subclasses of ABC scoring rules in the standard ABC election model, thereby both extending the result for ABC ranking rules to the standard setting and refining it to subclasses. In more detail, by relying on a consistency axiom for variable electorates, we characterize (i) the prominent class of Thiele rules and (ii) a new class of ABC voting rules called ballot size weighted approval voting. Based on these theorems, we also infer characterizations of three well-known ABC voting rules, namely multi-winner approval voting, proportional approval voting, and satisfaction approval voting.

LR-XFL: Logical Reasoning-Based Explainable Federated Learning

Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients’ local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model’s robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

Learning to Pivot as a Smart Expert

Linear programming has been practically solved mainly by simplex and interior point methods. Compared with the weakly polynomial complexity obtained by the interior point methods, the existence of strongly polynomial bounds for the length of the pivot path generated by the simplex methods remains a mystery. In this paper, we propose two novel pivot experts that leverage both global and local information of the linear programming instances for the primal simplex method and show their excellent performance numerically. The experts can be regarded as a benchmark to evaluate the performance of classical pivot rules, although they are hard to directly implement. To tackle this challenge, we employ a graph convolutional neural network model, trained via imitation learning, to mimic the behavior of the pivot expert. Our pivot rule, learned empirically, displays a significant advantage over conventional methods in various linear programming problems, as demonstrated through a series of rigorous experiments.

EmFORE: Learning Email Folder Classification Rules by Demonstration

Tools that help with email folder management are limited, as users have to manually write rules to assign emails to folders. We present EMFORE, an iterative learning system that automatically learns and updates such rules from observations. EMFORE is fast enough to suggest and update rules in real time and suppresses mails with low confidence to reduce the number of false positives. EMFORE can use different rule grammars, and thus be adapted to different clients, without changing the user experience. Previous methods do not learn rules, require complete retraining or multiple new examples after making a mistake, and do not distinguish between inbox and other folders. EMFORE learns rules incrementally and can make the neutral decision of leaving emails in the inbox, making it an ideal candidate for integration in email clients.