Classification Rules Research Articles

An intelligent rule-oriented framework for extracting key factors for grants scholarships in higher education

Education is a fundamental sector in all countries, where in some countries students com-pete to get an educational grant due to its high cost. The incorporation of artificial intelli-gence in education holds great promise for the advancement of educational systems and pro-cesses. Educational data mining involves the analysis of data generated within educational environments to extract valuable insights into student performance and other factors that enhance teaching and learning. This paper aims to analyze the factors influencing students' performance and consequently, assist granting organizations in selecting suitable students in the Arab region (Jordan as a use case). The problem was addressed using a rule-based tech-nique to facilitate the utilization and implementation of a decision support system. To this end, three classical rule induction algorithms, namely PART, JRip, and RIDOR, were em-ployed. The data utilized in this study was collected from undergraduate students at the University of Jordan from 2010 to 2020. The constructed models were evaluated based on metrics such as accuracy, recall, precision, and f1-score. The findings indicate that the JRip algorithm outperformed PART and RIDOR in most of the datasets based on f1-score metric. The interpreted decision rules of the best models reveal that both features; the average study years and high school averages play vital roles in deciding which students should receive scholarships. The paper concludes with several suggested implications to support and en-hance the decision-making process of granting agencies in the realm of higher education.

Clinical implications of VUS reclassification in a single-centre series from application of ACMG/AMP classification rules specified for BRCA1/2

BackgroundBRCA1/2 testing is crucial to guide clinical decisions in patients with hereditary breast/ovarian cancer, but detection of variants of uncertain significance (VUSs) prevents proper management of carriers. The ENIGMA (Evidence-based...

Implementation of the Good Behavior Game in Polish Elementary Schools Under COVID-19 Restrictions

Objectives of the research: The main aim of the research was to verify the quality of the implementation of the “Good Behavior Game” (GBG) program in Polish schools and to determine its effects on students and teachers during the COVID-19 pandemic.
 Research methods: The methodology of the research was based on qualitative and quantitative data analysis. The data was retrieved from teachers’ and coaches’ assessments and the results of playing the GBG in 19 elementary schools in Krakow, Poland during four months of the COVID-19 pandemic.
 A short description of the context of the issue: The Good Behavior Game is an American universal prevention program that can limit the risky behavior of adolescents and young adults and can help children starting their education to play the role of a student. For teachers, it is additionally an effective tool for classroom management. It is based on four basic pillars: class rules, teamwork, positive reinforcement, and observing students’ behavior.
 Research findings: The results show the obstacles and difficulties involved in implementing the GBG under COVID-19 restrictions. The findings show how the actions of teachers and coaches translated into an observable reduction in pupils’ disruptive behavior.
 Conclusions and/or recommendations: The analysis revealed a gradual increase in the quality of program implementation in all areas of evaluation. In terms of the program’s effect on the pupils, the findings showed that over time they gradually successfully internalized the rules and were behaving in line with expectations. Using the GBG in the context of online education caused by the restrictions of the COVID-19 pandemic posed a challenge to teachers: they had to change teaching methods, prepare new educational resources, and modify GBG procedures. The context of the pandemic can also be seen as an added value in the education process. Better digital competences and an innovative attitude toward the education process were observed among both teachers and students.

Two novel three-way decision models based on fuzzy [formula omitted]-covering rough sets and prospect theory under q-rung orthopair fuzzy environments

Three-way decision (TWD) and prospect theory (PT) are efficacious theories for investigating uncertain problems. TWD can mitigate the risks associated with rapid decision-making in traditional decision-making. PT can describe the psychological behavior of decision-makers. Furthermore, q-rung orthopair fuzzy sets (q-ROFSs) exhibit wider-ranging applications since they generalize intuitive fuzzy sets and Pythagorean fuzzy sets and unify them under one framework. However, the acquisition of q-ROFSs is an important issue in practical problems. In light of these considerations, this paper develops two TWD models under q-rung orthopair fuzzy environments, employing the principles of PT. To begin, we introduce two novel fuzzy β-covering rough sets that meet the inclusion property. Subsequently, utilizing the proposed fuzzy rough set, we provide an objective methodology to acquire q-ROFSs. Following this, the relative utility function is given through a combination of the value function and EDAS method. Additionally, the weighting function is constructed using two distinct approaches: one that continues to incorporate the ideas of EDAS method, and the other that utilizes the probabilistic dominance α-neighborhood classes to calculate conditional probabilities. After that, classification rules and sorting rules are derived. Ultimately, we substantiate the validity and excellence of the proposed TWD models through comparative and experimental analysis.

Mining Keys for Graphs

Keys for graphs are a class of data quality rules that use topological and value constraints to uniquely identify entities in a data graph. They have been studied to support object identification, knowledge fusion, data deduplication, and social network reconciliation. Manual specification and discovery of graph keys is tedious and infeasible over large-scale graphs. To make GKeys useful in practice, we study the GKey discovery problem, and present GKMiner, an algorithm that mines keys over graphs. Our algorithm discovers keys in a graph via frequent subgraph expansion, and notably, identifies recursive keys, i.e., where the unique identification of an entity type is dependent upon the identification of another entity type. We introduce the key properties, minimality and support, which effectively help to reduce the space of candidate keys. GKMiner uses a set of auxillary structures to summarize an input graph, and to identify likely candidate keys for greater pruning efficiency and evaluation of the search space. Our evaluation shows that identifying and using recursive keys in entity linking, lead to improved accuracy, over keys found using existing graph key mining techniques.

Smartphone application assisted optoelectronic nose for monitoring milk freshness

This study introduces an innovative method for detecting the freshness of milk through the use of smart packaging and a smartphone application. The proposed technique employs a colorimetric sensor array and a decision tree classifier, with a curvature test for feature selection. The freshness of milk samples was assessed using six colorimetric sensors, of which three, namely (Methyl violet, Chlorophenol red, and Nile blue) were chosen based on their classification error rates of 4%, 35.67%, and 8.33%, respectively. Sensor 1 was ultimately selected due to its superior performance, and its color information, along with the decision tree classification rules, was incorporated into a smartphone application. The application underwent an evaluation process, during which it was tested on twenty milk samples until total bacterial counts (TBC) reach 7 log CFU.g−1. The results showed that the application achieved a zero-error rate in identifying each sample, with the exception of 4–4.5 log CFU.g−1, where an error rate of 35% was observed. Overall, the total error rate for the entire evaluation period was 3%. Given these results, it can be concluded that this application has the potential to transform milk quality control and improve food safety.

Reconstructing Image Composition: Computation of Leading Lines.

The composition of an image is a critical element chosen by the author to construct an image that conveys a narrative and related emotions. Other key elements include framing, lighting, and colors. Assessing classical and simple composition rules in an image, such as the well-known "rule of thirds", has proven effective in evaluating the aesthetic quality of an image. It is widely acknowledged that composition is emphasized by the presence of leading lines. While these leading lines may not be explicitly visible in the image, they connect key points within the image and can also serve as boundaries between different areas of the image. For instance, the boundary between the sky and the ground can be considered a leading line in the image. Making the image's composition explicit through a set of leading lines is valuable when analyzing an image or assisting in photography. To the best of our knowledge, no computational method has been proposed to trace image leading lines. We conducted user studies to assess the agreement among image experts when requesting them to draw leading lines on images. According to these studies, which demonstrate that experts concur in identifying leading lines, this paper introduces a fully automatic computational method for recovering the leading lines that underlie the image's composition. Our method consists of two steps: firstly, based on feature detection, potential weighted leading lines are established; secondly, these weighted leading lines are grouped to generate the leading lines of the image. We evaluate our method through both subjective and objective studies, and we propose an objective metric to compare two sets of leading lines.

Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods

COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.

Identification of key gene expression associated with quality of life after recovery from COVID-19.

Post-acute sequelae of COVID-19 (PASC) is a persistent complication of severe acute respiratory syndrome coronavirus 2 infection that includes symptoms, such as fatigue, cognitive impairment, and respiratory distress. These symptoms severely affect the quality of life of patients after their recovery from COVID-19. In this study, a group of machine learning algorithms analyzed the whole blood RNA-seq data from patients with different PASC levels. The purpose of this analysis was to identify the gene markers associated with PASC and the special expression patterns for different PASC levels. By comparing the quality of life of patients after the acute phase of COVID-19 and before the disease, samples in the dataset were divided into three groups, namely, "Better," "The Same," and "Worse." Each patient was represented by the expression levels of 58,929 genes. The machine learning-based workflow included six feature-ranking algorithms, incremental feature selection (IFS), and four classification algorithms. The feature ranking algorithms were in charge of assessing feature importance, whereas IFS with classification algorithms were used to extract essential genes and to construct efficient classifiers and classification rules. The expression of top genes in the results was associated with the immune response to viral infection, which is supported by the published literature. For example, patients with low CCDC18 expression and high CPED1 expression had good quality of life, whereas those with low CDC16 expression had poor quality of life.

Three to one – an ethicolegal outline of mitochondrial donation in the South African context

Mitochondrial donation or mitochondrial transfer enables a woman with mitochondrial disease to have a genetically related child without transmitting the disease to the child. The techniques used for mitochondrial donation or transfer which are maternal spindle transfer or pro-nuclei transfer, require three gametes to ultimately produce a healthy embryo. Both these techniques result in the child inheriting nuclear DNA from the intending parents and mitochondrial DNA from the female donor. Following the legalisation of mitochondrial donation in the UK, after a rigorous process of scientific and ethical review, and the birth of another baby using the technique, coupled with the fact that there is no cure for mitochondrial disease, suggests that it is prudent for us to consider this reproductive intervention and its application in the South African setting. In addition, the 2019 UNESCO Report of the International Bioethics Committee (IBC) on assisted reproductive technologies (ART) and parenthood, encourages debate on changes in models of parenthood influenced by ART including the emergence of new models of families and forms of parenthood that extend beyond the classical rule of mater semper certa est (the mother is always known). ART, in particular mitochondrial donation, challenges this rule. The aim of this paper is to provide an outline of the legal and ethical positions of mitochondrial donation and resume the discussion with specific focus on the South African context.

RECORDING OF RESCUE ACTIVITY OF FIRE PROTECTION UNITS

The interventions of fire protection units (FPUs) are the response of the state security system toemergent threats characteristically inscribed as appropriate to be dealt with by the FPU. All rescueinterventions undertaken by the FPU are recorded in the Decision Support System of the State FireService (SWD SFS), where an ongoing record of all current incidents is kept. Each of the incidentshandled by the FPU, after a multi-level verification of the baseline information concerning a giventhreat, is classified in accordance with the applicable Principles (Headquarters of the State FireService, 2022) as: a fire (F), a local threat (LT), false alarm (FA).The types of incidents handled by the Fire Protection Units (FPUs) and the answer to the questionof whether the adopted classification fully captures the essence of the FPU intervention will bethe subject of discussion by the authors who will analyse the area in question with referenceto both the actual conditions and the formal and legal status. In compliance with the currentlegislation, the authors will present legal boundaries for the commencement of a rescue actionwith an indication of its systematic allocation to the appropriate group of incidents (P, MZ, AF),while demonstrating the importance of the correct identification of circumstances indicating itstermination and the proper documentation of the above fact. In the subsequent part of the article,the authors propose to correct the classification of the type of incidents undertaken by the FPUs,stating that they may be recorded taking into account events of an authoritative nature as well asthose intended to support the activities of other services, entities and inspections. The authors’scientific considerations in the final part of the article will be presented in the form of clarifiedproposals for the revision of the rules for the generic classification of events undertaken by FPUs.

A new approach to extract the upright maize straw from Sentinel-2 satellite imagery using new straw indices

Unharvested upright maize straw is highly susceptible to burning in winter or in spring of the following year, contributing significantly to air pollution. The rapid and accurate acquisition of the spatial distribution of upright maize straw holds great importance for agricultural and environmental protection departments. This study introduces a new approach that focuses on extracting upright maize straw using Sentinel-2 satellite images. Initially, the Sentinel-2 images from the Botou and Hejian regions underwent preprocessing. To minimize interference during straw extraction, the normalized difference vegetation index (NDVI) was utilized to extract and eliminate built-up land and woodland areas. Subsequently, utilizing the spectral characteristics of maize straw across various bands including short-wave infrared, red edge, and near-infrared, three new indices were proposed and computed: the accumulation infrared straw index (AIRSI), the product near-infrared straw index (PNISI), and the normalized difference short-wave infrared straw index (NDSSI). Finally, the decision tree classification method was implemented to establish classification rules and extract the spatial distribution of upright maize straw. The study results revealed that the proposed new straw indices achieved an extraction accuracy of over 90% for upright maize straw. This new approach effectively enhances the identification accuracy of upright maize straw, providing valuable support for scientific supervision, comprehensive utilization, and prevention and control measures against maize straw burning.

Predictive analysis of recycled concrete properties at elevated temperatures using M5 pruned rule classifiers

Predictive analysis of recycled concrete properties at elevated temperatures using M5 pruned rule classifiers

Splitting Tuples of Mismatched Entities

There has been a host of work on entity resolution (ER), to identify tuples that refer to the same entity. This paper studies the inverse of ER, to identify tuples to which distinct real-world entities are matched by mistake, and split such tuples into a set of tuples, one for each entity. We formulate the tuple splitting problem. We propose a scheme to decide what tuples to split and what tuples to correct without splitting, fix errors/assign attribute values to the split tuples, and impute missing values. The scheme introduces a class of rules, which embed predicates for aligning entities across relations and knowledge graphs G, assessing correlation between attributes, and extracting data from G. It unifies logic deduction, correlation models, and data extraction by chasing the data with the rules. We train machine learning models to assess attribute correlation and predict missing values. We develop algorithms for the tuple splitting scheme. Using real-life data, we empirically verify that the scheme is efficient and accurate, with F-measure 0.92 on average.

Problems of assessment and analysis of mortality rates in the context of the rules of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)

Purpose of the study: to study changes in the structure of causes of death among those who died aged 18 years and older according to medical death certificates (MDC) and to discuss the problems of assessing the contribution of certain groups of diseases and conditions to the mortality of the population. Materials and methods. The source of information was the electronic database of the Main Directorate of the Civil Registry Office (USR registry office of the Moscow Region) of the MSS for those who died at the age of 18 and older for 2019, 2021 and 2022. one of the subjects of the Russian Federation. Results: All 3 years 99 % of deaths occur in 12 classes of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); 3 classes (diseases of the circulatory system – DCS, nervous system – DNS and neoplasms) accounted for 70 % of deaths in 2019, 58 % in 2021 and 62 % in 2022 (the share decreased due to COVID-19). The share of DCS was 40 %, 32 % and 37.7 %; neoplasms – 15.3 % 10.9 % and 13.5 %. The greatest decrease in mortality was noted in the class of mental and behavioural disorders (MBD; 8 times: 58.4 in 2019 and 7.4 per 100 thousand in 2022). The proportion of male deaths over all 3 years was higher in the classes of injury and poisoning (IP), infectious diseases (ID), diseases of the respiratory system (DRS) and digestive system diseases (DDS). The highest age of the deceased was registered according to the classes of DNS and diseases of the genitourinary system (DGS); the youngest age in ID and IP classes. For all 3 years, more than 60 % of deaths in a hospital are registered in the classes of ID and DDS, and in 2021 and 2022 from COVID-19. Deaths at home predominate in the class of DNS and ill-defined conditions (class R). For individual codes in the classes of DCS, DNS, MBD, DGS, representing non-prohibited but unspecified causes of death, significant changes (hundreds and thousands of cases) were revealed in dynamics, which is probably due to a change in approaches to coding primary cause of death and affects the structure of mortality by classes and individual reasons. Conclusions. Significant annual changes in the number of deaths from individual causes, differences in demographic characteristics by ICD classes require clarification of the criteria for using individual 4-digit codes and subsequent analysis of mortality for clinically related groups of diseases.

Analysis of mental health influencing factors and students’ psychological education countermeasures based on ID3 algorithm

Abstract This paper utilizes the data mining decision tree ID3 algorithm to improve the traditional ID3 algorithm, exclude the influence of other factors, and realize the decision tree ID3 algorithm by using the data mining decision tree ID3 algorithm in the database of college students’ mental health assessment in the application of mental health assessment in colleges and universities as an example. Among them, the number of nodes, the number of rules, the classification accuracy and the time of constructing the decision tree of the algorithm are compared to verify the improvement effect of the ID3 algorithm. The target dataset consists of psychological assessment data of students, which includes their basic situation and nine-dimensional psychological symptoms. Analyze the recorded data of students’ mental health status, extract the information on personality, parental relationship, economic income, and psychological abnormality, set the decision tree analysis variables, assign the value of students’ health status, and derive the specific factors affecting students’ mental health by using the decision tree If-Then classification rules. The validation results show that the generated decision tree ID3 model cross-validation estimate is 0.261, the standard error is 0.016, and its obtained standard error is less than 0.018, which indicates that the model fits better.

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.

Classification and simplified design rules concerning the moisture performance of capillary active insulation materials

Conventional interior insulation systems protect the wall from condensation in winter by the help of vapor barriers or vapor retarding insulation materials. However, these solutions also reduce the drying potential of the walls towards the inside. This can be a disadvantage if wetting from the outside due to rain or other influences takes place and the drying potential to the outside is not sufficient. In such cases, so called capillary active materials can be an alternative, because they provide additional drying potential to the inside in summer thanks to a rather low vapor diffusion resistance. In winter they work without vapor retarders because they can store and wick water away from the condensation plane back towards the interior by capillary action. In practice, there are currently many different materials under the label "capillary active" with a correspondingly wide range in terms of moisture storage, liquid transport and vapor diffusion resistance and it is difficult to decide, whether material is suitable for an application or not. There is still missing a common definition of the performance of such materials. While for most other materials already simplified design and application rules for the application as interior insulation are available, capillary active interior insulation materials still require an individual design, normally by the help of hygrothermal simulation. In order to simplify also their practice application, a classification of the moisture performance of capillary active interior insulation, considering the influencing parameters of relevance on the moisture level at the critical interface between wall and interior insulation was developed. On this basis, also a simplified verification process as well as design rules for the main practical application fields become possible, which allows designers to choose an appropriate system for the specific situation.

Optimization deep learning with rough set approach model classification Otitis

<span>Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.</span>

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

<p>Traditional markets are public service facilities that can be utilized by the<br />community. The market function is used place where sellers and buyers meet<br />in conducting transactions. This study aims to build a machine learning<br />classification analysis model in measuring community satisfaction with<br />traditional market facilities. The analytical methods used include Fuzzy.<br />multiple linear regression (MRL), artificial neural network (ANN), and<br />decision tree (DT). Fuzzy is used to generate a pattern of rules in determining<br />the level of satisfaction. MRL serves to measure and test the correlation of<br />rules that have been formed. The ANN method is used to carry out the<br />classification analysis process based on learning. In the final stage. DT is used<br />to describe the decision tree of the analysis process. This study presents the<br />results of machine learning analysis which is very good in determining<br />satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy<br />logic which can develop a classification rule pattern of 32 patterns. MRL also<br />shows a significant correlation level of 81.1% based on the indicator variables.<br />Overall, the machine learning classification analysis model can provide<br />knowledge to be considered in the management of traditional markets as<br />public service facilities.</p>