An Elective Course Decision Support System Using Decision Tree and Fuzzy Logic

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Professional education aims to provide students with requisite knowledge and skills to face today's competitive environment. To achieve this currently practices like systematically assessing student learning and measuring learning outcomes are presently in place. The primary goal achieved by this process is the continued improvement of academic quality for the institution. This paper investigates the application of correlation analysis for finding relationships between skills acquired is one test with the other components like assignments, individual marks with the final result and the correlation of performances of students' across courses. The results obtained for the first study indicates that there exists moderately strong relationship. The second study shows that correlation coefficients of related courses are better while that of some unrelated courses have moderate value. This means that in case of related courses the skills acquired in a prerequisite course can help them to do better in the current course. This exploratory analysis when properly interpreted can help plan and retrospect and re-adjust the mode of assessment or course delivery to enhance student learning.

The current stage of Georgia-Russia relations began in 2012, when the "Georgian Dream" party came to power and introduced changes in the foreign policy course. In order to understand the current course of bilateral relations, it is important to analyze the processes that have taken place between the parties since 2012. The purpose of the scientific work is to analyze the bilateral relations after the "Georgian Dream" came to power, to present the existing problems and challenges, to investigate the nature of these relations, as well as the influence of geopolitical developments on the relations. While writing the scientific work, we used the systematic methods of historical-comparative, event and content analysis. Achievements. Since 2012, relations between Georgia and the Russian Federation have registered a dynamic growth in several areas, changing the character and course of relations, the Russian Federation has become one of the leading economic states of Georgia, but politically, the existing problems the ongoing conflicts with Abkhazia and Ossetia in bilateral relations remain unresolved.

By analyzing 63 English-speaking institutions that offer ALA-accredited master’s programs in library and information studies, this research aims to explore the education for knowl­edge organization (KO) at different levels and across fields. This research examines the KO courses that are the required courses and elective courses in the MLIS programs, that are offered in other master’s programs and graduate certificate programs, that are adapted to the undergraduate degree and certificate programs, and that are particularly developed for programs other than MLIS. The findings indicate that the great majority of MLIS programs still have a focus on or a significant component of knowl­edge organization as their required course and include the knowl­edge organization elective courses, particularly library cataloging and classification, on their curriculum. However, there is a variety of the offerings of KO related courses across the programs in an institution or in the same program across the institutions. It shows a promising trend that the traditional and new KO courses play an important role in many other programs, at different levels and across fields. With the conventional, adapted, or innovative content, these courses demonstrate that the principles and skills of knowl­edge organization are applicable to a wide variety of settings, can be integrated with other disciplinary knowl­edge and emerging technologies, and meet the needs of different career pathways and groups of learners.

A curriculum is a set of related courses that constitutes the basis of a degree program. The required courses of a curriculum generally build student knowledge and skills particular to the field. In most cases, these are cumulative, meaning that as students go through their studies, they put their new knowledge on top of earlier ones, hence leading to the notion of prerequisite courses that must precede a given course. As accreditation practices gain widespread acceptance, and as uniformity among peer institutions is promoted to facilitate mobility, each course is assigned a set of learning outcomes. The learning outcomes of a prerequisite course are seen to encapsulate the skills necessary to take the downstream course. This study follows our efforts regarding the substantial revision of engineering courses throughout our college. As the task is quite involved, we developed a flexible linear programming-based tool to help the decision-making process by quickly evaluating alternative curricula. This study aims to provide an effective decision-making tool to accommodate many “what if” scenarios which would provide options to the decision-makers and help them detecting inconsistencies and oversights. This paper describes our approach and our experiences.

Esse artigo apresenta um estudo que teve como objetivo identificar as concepções de estudantes ingressantes no Curso de Licenciatura em Pedagogia da Universidade Estadual de Mato Grosso do Sul (UEMS), de Dourados, Mato Grosso do Sul, sobre bebês e sua educação em espaços coletivos. A importância dessa investigação é justificada na medida em que as concepções mapeadas influenciam a maneira como se organiza as práticas pedagógicas com os bebês nas instituições de educação infantil. Participaram desse estudo treze estudantes do primeiro ano do referido curso, as quais optaram por se matricular na disciplina eletiva denominada “Conhecimentos e saberes sobre as práticas com bebês na educação infantil”. A coleta de dados foi feita mediante aplicação de um questionário e ocorreu no início do ano letivo. Conhecer as concepções das ingressantes no curso em relação ao trabalho pedagógico com bebês é importante, pois contribui para a reflexão sobre a formação inicial de professores na Universidade, possibilitando discussões que promovam o repensar de ideias que possam não ser adequadas à sua formação acadêmica.

Compared with the curriculum system of colleges and universities, public elective courses are an essential part, which can extend the scope of students' knowledge to the greatest extent, enhance their humanistic quality and correct their conduct. However, in the current public elective courses of colleges and universities, the problems concerning the integration and management of teaching resources are serious, and the teaching resources cannot be fully utilized. The article thoroughly discusses the problem of integrated management of teaching resources for public elective courses, formulates effective solutions based on the problems, and fully demonstrates the value of higher education.

This paper intends to facilitate academic advising by means of optimal long‐term course planning (LTCP), which is instrumental in students' retention and success. LTCP ensures that students will satisfy the educational requirements without facing unnecessary delays. The proposed decision support system is based on an optimization model which deals with several student preferences, that is, maximum credits per semester, difficulty of courses, graduation semester, and elective courses priorities. The proposed system has been implemented in an industrial engineering department, and several improvements and modifications have been done based on student feedback. The developed system supports two kind of users, that is, educational staff and the students. The courses, their relationships, and regulation parameters are defined/set by educational staff, whereas students determine their own information and preferences, for example, finished courses, graduation target, interest for each elective course, complexity of courses, course load and complexity for each semester, and relative weights of objective functions. Considering theoretical issues, common applications have been integrated to form this system with simple and user‐friendly interfaces.

The growing amount of available data generates complex problems when they need to be treated. Usually these data come from different sources and inform about different issues, however, in many occasions these data can be interrelated in order to gather strategic information that is useful for Decision Making processes in multitude of business. For a qualitatively and quantitatively analysis of a complex Decision Making process is critical to employ a correct method due to the large number of operations required. With this purpose, this chapter presents an approach employing Binary Decision Diagram applied to the Logical Decision Tree. It allows addressing a Main Problem by establishing different causes, called Basic Causes and their interrelations. The cases that have a large number of Basic Causes generate important computational costs because it is a NP-hard type problem. Moreover, this chapter presents a new approach in order to analyze big Logical Decision Trees. However, the size of the Logical Decision Trees is not the unique factor that affects to the computational cost but the procedure of resolution can widely vary this cost (ordination of Basic Causes, number of AND/OR gates, etc.) A new approach to reduce the complexity of the problem is hereby presented. It makes use of data derived from simpler problems that requires less computational costs for obtaining a good solution. An exact solution is not provided by this method but the approximations achieved have a low deviation from the exact.

The paper offers an estimation of the complexity of the constructed logical tree structure for classifying an arbitrary case in the conditions of a strong class division of the initial training sample. The principal solution to this question is of a defining nature, regarding the assessment of the structural complexity of classification models (in the form of tree-like structures of LCT/ACT) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of post-pruning structure. The presented research is relevant not only for constructions (structures) of logical classification trees, but also allows us to extend the scheme of complexity estimation to the General case of algorithmic structures (ACT models) of classification trees (the concept of algorithm trees and trees of generalized features - TGF). Is investigated the actual question of the concept of decision trees (tree recognition) – evaluation of the maximum complexity of the General scheme of constructing a logical tree based classification procedure of stepwise selection of sets of elementary features (they can be diverse sets and combinations) that for given initial training sample (array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) are estimated at each stage of the scheme of the model in this sample for the case of strong separation of classes. Modern information systems and technologies based on mathematical approaches (models) of pattern recognition (structures of logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information, and this is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods, schemes and achieve a fundamentally new result. The research is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features (individual features and their combinations) built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent instrumental analysis (the final decomposition of the model).

KASP marker technology has been used in molecular marker-assisted breeding because of its high efficiency and flexibility, and an intelligent evaluation model of KASP marker primer typing results is essential to improve the efficiency of marker development on a large scale. To this end, this paper proposes a gene population delineation method based on NTC identification module and data distribution judgment module to improve the accuracy of K-Means clustering, and introduces a decision tree to construct the KASP-IEva primer typing evaluation model. The model firstly designs the NTC identification module and data distribution judgment module to extract four types of data, grouping and categorizing to achieve the improvement of the distinguishability of amplification product signals; secondly, the K-Means algorithm is used to aggregate and classify the data, to visualize the five aggregated clusters and to obtain the morphology location eigenvalues; lastly, the evaluation criteria for the typing effect level are constructed, and the logical decision tree is used to make conditional discrimination on the eigenvalues in order to realize the score prediction. The performance of the model was tested by the KASP marker typing test results of 2519 groups of cotton varieties, and the following conclusions were obtained: the model is able to visualize the aggregation and classification effects of the amplification products of NTC, pure genotypes, heterozygous genotypes, and untyped genotypes, enabling rapid and accurate KASP marker typing evaluation. Comparing and analyzing the model evaluation results with the expert evaluation results, the average accuracy rate of the four grades evaluated by the model was 87%, and the overall evaluation results showed an uneven distribution of the grades with significant differential characteristics. When evaluating 2519 KASP fractal maps, the expert evaluation consumes 15 hours, and the model evaluation only uses 8min27.45s, which makes the model intelligent evaluation significantly better than the expert evaluation from the perspective of time. The establishment of the model will further enhance the application of KASP markers in molecular marker-assisted breeding and provide technical support for the large-scale screening and identification of excellent genotypes.

In order to precisely manage and control rural credit risks, this study delved deeply into the assessment model predicated on machine learning algorithms. During the data collection phase, 10000 copies of farmers' credit records spanning nearly five years were procured from financial institutions such as rural credit cooperatives and agricultural banks, encompassing over 20 characteristic data items including age, income, land area, and past loan repayment situations. After data cleansing to handle missing values and outliers, the training set and test set were partitioned in a 7:3 ratio. The logical regression, decision tree, random forest, and support vector machine algorithms were employed for modeling. The logical regression optimized parameters via the gradient descent method; the decision tree selected splitting attributes based on information gain; the random forest constructed 100 decision trees and conducted comprehensive voting; the support vector machine mapped data to a high-dimensional space through kernel functions to seek the optimal classification hyperplane. It was experimentally discovered that the random forest algorithm exhibited optimal performance, with an astonishingly high accuracy rate of 85% on the test set, a recall rate of 82%, and an F1 value reaching 0.83, significantly outperforming other algorithms. This model provides robust support for rural credit decision-making by financial institutions, holds the promise of reducing the non-performing loan rate, and propels the healthy development of rural finance.

Yazma, 2019 Türkçe Dersi Öğretim Programı’nda belirtildiği üzere dört temel beceriden biridir. Programda yazma becerisine yönelik kazanımlar yer almaktadır. Ayrıca ortaokullarda seçmeli bir ders bulunmakta bu dersle ilgili ayrıntılar 2018 Yazarlık ve Yazma Becerileri Dersi Programı’nda yer almaktadır. Tarihsel sürece bakıldığında ise dönemin öğretim programı niteliğinde olan Salname-i Nezaret-i Maarif-i Umumiye (Maarif Salnamesi) eğitim yıllıklarında Türkçe dersi ve çeşitli başlıklar altında yazma becerisine yönelik dersler yer almaktadır. Bu çalışma doküman incelemesi yöntemiyle 1316 H Maarif Salnamesi ile 2019 Türkçe Dersi Öğretim Programı ve 2018 Yazarlık ve Yazma Becerileri Dersi Programı’nın, Türkçe dersi yazma becerisi açısından incelenmesiyle oluşturulmuştur. Çalışmada önce 1316 H Maarif Salnamesi günümüz Türkçesine aktarılmış; sonra burada yer alan Türkçe dersleri ve yazma becerisine yönelik dersler tespit edilmiştir. Ardından da programların karşılaştırmalı incelemesi yapılarak yazma becerisinin programlardaki değişimi ve gelişimi değerlendirilmeye çalışılmıştır. Sonuç olarak görülmektedir ki 1316 H Maarif Salnamesi’nde yer alan ders cetvellerinde yazma becerisine yönelik lügat ve imla, imla, kıraat ve imla, kitabet, hüsn-i hat gibi dersler yer almıştır. Derslerin açıklamalarında ise dersin işleniş aşamaları, metnin özellikleri, öğretmenin rolü, öğrencilere verilecek konu ve ödevler gibi ayrıntılar verilmektedir. Güncel ders cetvellerinde Türkçe zorunlu ders iken; yazarlık ve yazma becerileri dersi, seçmeli derstir. 2019 Türkçe Dersi Programı’nda yazma kazanımı ve açıklamalarına yer verilip; yöntem ve teknik açısından ayrıntılara yer verilmez iken; 2018 Yazarlık ve Yazma Becerileri Dersi Programı’nda süreç daha da ayrıntılandırılır. Ölçme değerlendirme bakımından ise programlarda benzer bir yaklaşımın benimsendiği görülür. Buna göre süreç temelli değerlendirmenin tercih edildiği belirtilmekte ve yapılacak sınavlarla öğrencilerin bir üst kademeye geçme başarısı ölçülmektedir.

Four years ago, at BOSC 2019, the Forome Platform team first presented AnFiSA, an open-source variant curation and interpretation tool for analysis of the data produced from genome sequencing. In the last year the work has been published in the Journal of Biomedical Informatics1. One of the focuses of the paper was the creation of a community comprising clinicians, researchers, and professional software developers to reach our goals. We received contributions from numerous organizations and dozens of individual contributors to facilitate this effort. It became evident that for a community to operate efficiently, there must be a shared language that comes naturally to all members. Back in 2019, we argued that representing variant curation guidelines in the form of manually curated decision trees would align the guidelines development workflow with that of software development. We also suggested that developers of these guidelines would benefit from utilizing an Integrated Development Environment (IDE) that offers functionalities such as syntax highlighting, error checking, and visual debugging. Over the past four years, we have made significant progress towards creating such an IDE. We replaced the Vue.js client with one based on React.js and introduced IDE-like features. As the guidelines are often articulated as free-text English prose, we contend that developing a more structured domain-specific language (DSL) that can be grasped by both clinicians and engineers/researchers would be immensely advantageous for the community as well as helpful in mimicking software development process. Our development of the DSL and its corresponding visual representations for editing and debugging allowed us to condense decision trees into ordered sequences of inclusion and exclusion criteria. In this talk, we aim to present our systematic approach for converting free text into complex logical statements and extensive decision trees, which can be further translated into inclusion and exclusion criteria depicted by graphical widgets. We will also demonstrate the utilization of these widgets and dashboards in a debugging process for a particular corpus of data. We would like to emphasize that we view the visualization of the ordered sequences of inclusion and exclusion criteria as a valuable tool, extending beyond variant curation to areas such as patient recruitment and population health research. Our DSL utilizes genetic and sequencing annotations as variables, which encompass both technical and biological information. Technical annotations include provenance and confidence data for specific calls, while biological annotations summarize evidence pertaining to the potential impact of mutations on molecular function and phenotype. These annotations collectively provide clinicians and researchers with information on provenance, confidence, and evidence to aid in decision making. Our work on the DSL revealed the need to structure annotation types systematically to help answer three critical questions: 1) How confident am I that the variant is present in my patient? 2) How confident am I that it affects the phenotype? 3) What evidence supports the association between the observed genotype change and the phenotype? In order to accomplish this objective, in the latest version of AnFiSA we have implemented a classification system for annotations based on their scale and resolution, knowledge domain, and method. Genetic and biological annotations gather relevant evidence from public and proprietary sources about the potential mutation effects on molecular function and phenotype, combining multiple inputs to provide a summary. The strength of evidence heavily relies on the level of resolution of a specific annotation, as well as the knowledge domain and the method of an annotation2. The resolution ranges from granular transcript and variant specific to broader functional units. The knowledge domain refers to areas such as human or animal genetics, or molecular function described by experimental or computational techniques. The methods used for generating annotations vary among statistical genetics inference, bioinformatics predictions, and in vivo and in vitro experiments. The initial implementation of this classification is accessible on GitHub and can be tested in the most recent versions of AnFiSA. Our proposal is to initiate a conversation regarding the semantic and syntax of the DSL for variant curation and interpretation, as well as the most effective and intuitive way to structure annotation types. We believe that developing a standard or at least a guideline, for representation of variant curation rules will help in establishing a unified and systematic approach to reporting clinically relevant and actionable variants and ultimately will bring us closer to evidence based medicine. 1. Bouzinier, M. A. et al. AnFiSA: An open-source computational platform for the analysis of sequencing data for rare genetic disease. J. Biomed. Inform. 133, 104174 (2022). 2. Hufeng Zhou et al. FAVOR: Functional Annotation of Variants Online Resource and Annotator for Variation across the Human Genome. Nucleic Acids Res 2022 Nov 9; gkac966. PMID: 36350676. DOI: 10.1093/nar/gkac966

[Purpose] The purpose of this study is to examine the credit system and the corresponding graduation requirements of general high schools in China, and to present the associated issues and challenges. [Methods] The previous research related to the high school credit system and graduation requirements was analyzed, focusing on the literature. [Results] The analytical framework for this study is divided into graduation requirements, duration of study, grade promotion, and other related aspects. The findings based on this framework are as follows. First, to graduate, students must complete a minimum of 144 credits, which are categorized into mandatory courses, selective mandatory courses, and elective courses. The graduation exam can be taken from the second year of high school onwards, after meeting the minimum credit requirements set by the Ministry of Education's curriculum. The exam subjects vary monthly, and the results are classified into five grades. Courses are offered according to the national curriculum and the school's own curriculum.Second, high school education in China lasts for three years, and early graduation is generally not allowed.Third, the credit system is implemented alongside the grade-level system, so grade promotion is based on the grade-level system. Fourth, the comprehensive competency evaluation is conducted to observe, record, and analyze students' overall growth for character education purposes. For college entrance, there are two options: the 3+3 model and the 3+2+1 model. [Conclusion] FFirst, the graduation requirements include meeting the course completion standards and passing the graduation exam. Second, since the credit system aims to provide education tailored to individual aptitudes and talents, cases such as early graduation should be approached with caution. Third, as the credit system is currently implemented alongside the grade-level system, efforts are needed to fully realize the comprehensive implementation of the credit system. Fourth, it is necessary to enhance the comprehensive evaluation for the overall development of students, and continuously improve the college entrance evaluation system towards self-assessment and self-formation