The Use of Entropy Measure for Higher Quality Machine Learning Algorithms in Text Data Processing

Guseva, Anna I.; Kuznetsov, Ilya

doi:10.1109/ficloudw.2017.84

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The received unsatisfactory grades affect the subsequent study of related topics which, in turn, also leads to a negative result. Using the title and description of lessons in text analysis will allow selecting meaningful terms from the text and associating individual lessons with the subject classifier [11]. Thus, the knowledge gained by a student can be assessed and formalized in the context of the subject classifier.…”

Section: Resultsmentioning

confidence: 99%

Digital development trajectory as a tool for improving the quality of education

et al. 2021

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The present article proposes an approach to using students’ digital development trajectories to improve the quality of education. Designing a digital development trajectory has a positive effect on students’ motivation and active participation in deciding on its formation. The goal of the study is to create a conception of using methods and approaches of educational data mining and machine learning in analyzing and predicting digital development trajectories to improve the educational process quality. Implementation of learning management systems with the opportunity to dynamically track students’ academic performance allows timely correcting gaps appearing in students’ knowledge and thereby reducing the risk of them falling behind in their study group. Recording the obtained results of completed assignments and interactions with learning management systems also benefits teachers who get an opportunity to receive feedback in an implicit form and account for gaps appearing in the curriculum in a processed and aggregated form. The analysis is conducted based on a database of additional education in a primary school (grades 1-3). The present study examines several scenarios of using a digital development trajectory in education including students’ progress in an academic performance trajectory based on their grades, a trajectory in terms of time spent on solving problems to determine their complexity, and a heatmap for determining problematic areas and weak spots in each teacher’s educational course. Based on educational data from a learning management system, the authors present a set of recommendations for teachers on the adaptation of their educational course to students’ abilities promoting the increase of the overall quality of education.

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Section: Resultsmentioning

confidence: 99%

Digital development trajectory as a tool for improving the quality of education

et al. 2021

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“…To automatically identify agricultural diseases, including those that impact rice, corn [ 9 ], wheat [ 10 ], cotton [ 11 ], tomato [ 11 ], and cucumber [ 12 ], researchers are utilising image processing, machine learning, and other techniques. Numerous feature segmentation techniques, such as k-means clustering [ 10 ], fuzzy C-means [ 11 ], Roberts detection, Prewitt detection [ 12 ], and Sobel detection and extraction techniques [ 13 ], such as Tamura, Entropy [ 14 ], RMS [ 15 ], and Kurtosis [ 16 ], are used to detect diseases as a result of technological advancements [ 17 ]. These techniques allow farmers to automatically identify the various diseases that affect particular crops.…”

Section: Introductionmentioning

confidence: 99%

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

Bonkra

Bhatt

Rosak-Szyrocka

et al. 2023

IJERPH

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Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

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