scholarly journals A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance

2021 ◽  
Vol 16 (15) ◽  
pp. 31
Author(s):  
Ivan Henderson Vy Gue ◽  
Alexis Mervin Sy ◽  
Ailene Nuñez ◽  
Pocholo James Loresco ◽  
Jaychris Georgette Onia ◽  
...  
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Engineering Students ◽  
Rough Set Theory ◽  
Fuzzy Inference ◽  
Rule Induction ◽  
Retention Rates ◽  
Inference System ◽  
Linguistic Rules ◽  
Negative Attributes

Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

scholarly journals The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 11
Author(s):  
Mamman Mamuda ◽  
Saratha Sathasivan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Learning Ability ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Gradient Descent Algorithm

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

scholarly journals Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2822
Author(s):  
Tamas Galli ◽  
Francisco Chiclana ◽  
Francois Siewe
Keyword(s):  
Product Quality ◽  
Fuzzy Inference System ◽  
Software Maintenance ◽  
Fuzzy Inference ◽  
Quality Model ◽  
Inference System ◽  
Linguistic Rules ◽  
Software Product ◽  
Execution Tracing

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

An impact of simulation labs on engineering students' academic performance: a critical Investigation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naga Vamsi Krishna Jasti ◽  
Srinivas Kota ◽  
Venkataraman P.B.
Keyword(s):  
Academic Performance ◽  
Continuing Education ◽  
Student Learning ◽  
Student Performance ◽  
Engineering Students ◽  
Program Outcomes ◽  
Content Type ◽  
Theoretical Concepts ◽  
Application Analysis ◽  
The Impact

Purpose This paper aims to investigate the impact of simulation laboratory on continuing education engineering students’ academic performance. Design/methodology/approach The investigation consists of establishing the student learning levels then mapping the student learning levels (knowledge, comprehension, application, analysis, synthesis and evaluation) through program outcomes with appropriate evaluation components. 270 continuing education students enrolled during six years were selected to be observed as part of this study. These students were divided into two subgroups, one with 135 students who were offered simulation lab (G2) and the other 135 students were not offered simulation lab (G1) in this investigation. Subsequently, a comparative analysis was carried out on these two groups to assess the student performance in multiple evaluation components with respect to student learning level and program outcome achievement. Findings It was identified that student performance in the application, analysis, synthesis and evaluation learning levels has improved for the group with simulation lab, and no change or minimal change was observed for the group without simulation lab. It was revealed that the simulation lab practice problems needs to be aligned with the theoretical concepts in the course to get a better performance from the students. Originality/value The study was conducted in one of the leading institutes with 270 students’ performance observed over a period of six years. It is the comprehensive work done on a complete program with data collated over a period of six years in multiple courses and multiple assessments.

scholarly journals CLASSIFICATION OF SHORT TECHNICAL TEXTS USING SUGENO FUZZY INFERENCE SYSTEM

2021 ◽  
Vol 2021 (1) ◽  
pp. 16-27
Author(s):  
Andrei Viktorovich Borovsky ◽  
Elena Evgenievna Rakovskaya ◽  
Artem Leonidovich Bisikalo
Keyword(s):  
Fuzzy Inference ◽  
Functional Dependencies ◽  
Inference System ◽  
Operational Characteristics ◽  
Linguistic Rules ◽  
Design Documentation ◽  
Sugeno Model ◽  
Sets Theory ◽  
Technical Terms

The paper presents the results of classification of the short technical texts on the purpose of instruments using fuzzy sets theory and fuzzy logic. An important stage in designing special-purpose technical systems is the choice of equipment with specific operational characteristics. The need to categorize short technical texts, which present a brief description of equipment, annotations, fragments of databases, appears due to the fact that information about the equipment found in thematic abstract collections, technical and design documentation or in contextual advertising is often not structured and scattered. The other problems are a large number of typos, incorrect word usage and definitions in the texts. Much attention is paid to the characteristics of the objects of research and to recording their specific features – a large number of technical terms, abbreviations, symbols. The classifying technique is described, the expediency of application of fuzzy inference of Sugeno system associated with fuzziness of the natural language, the simplicity of mathematical calculations in the course of the experiment. A Sugeno model combines the description of the objects of research in the form of linguistic rules and functional dependencies. This approach greatly facilitates the interpretation of classification results

scholarly journals A fuzzy decision support system for meta-evaluation a new approach and a case study performed in Brazil

2007 ◽  
Vol 15 (56) ◽  
pp. 447-462 ◽  
Author(s):  
Ana Carolina Letichevsky ◽  
Marley Maria Bernardes Rebuzzi Vellasco ◽  
Ricardo Tanscheit
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Decision ◽  
Inference System ◽  
New Approach ◽  
Whole Process ◽  
Linguistic Rules ◽  
Data Collection Instrument ◽  
Fuzzy Decision Support System ◽  
Process Ability

This paper presents a new methodology for meta-evaluation that makes use of fuzzy sets and fuzzy logic. It is composed of a data collection instrument and of a hierarchical fuzzy inference system. The advantages of the proposed methodology are: (i) the instrument, which allows intermediate answers; (ii) the inference process ability to adapt to specific needs; and (iii) transparency, through the use of linguistic rules that facilitate both the understanding and the discussion of the whole process. The rules are based on guidelines established by the Joint Committee on Standards for Educational Evaluation (1994) and also represent the view of experts. The system can provide support to evaluators that may lack experience in meta-evaluation. A case study is presented as a validation of the proposed methodology.

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