Music plays a vital role in our lives. there is a huge amount of information available everywhere;our task is to filter out only the information that is relevant for users in which they are interested or which suits t...
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For the past few years, fast fashion has become very popular, which has had a great impact on the textile and fashion industries. Fashion is an integral part of one's daily lives, and it has a significant impact o...
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Withthe development of ultra-high voltage projects in China, the scale of power grids has been expanding. High-voltage switchgear is widely utilized in various substations and the power industry, gradually becoming a...
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Educational datamining (EDM) is not only a process of applying datamining algorithms on academic data. that is a process of exploring and providing solutions at various levels of educational system. that is useful f...
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ISBN:
(纸本)9789819700363
Educational datamining (EDM) is not only a process of applying datamining algorithms on academic data. that is a process of exploring and providing solutions at various levels of educational system. that is useful for students, educators, management, and administration for decision-making and preparing futuristic strategies. the proposed EDM framework is motivated to enhance the student academic performance. the focus is on threefold: first to identify the student learning behavior to support the students, and teacher provide the remedial actions on the weak students. We propose clustering method for the study of students’ learning behavior associated with positive and negative outcomes (in exams) by utilizing datamining techniques. Second, there is some classifying approach to characterize student based on performance measure that they earn in examination. Applying supervised classifier on the datasets, we have found significant improvement in results. Finally, the study turns toward developing a technique for recommending appropriate study material by calculating readiness of student and complexity of course material. To achieve the objectives, three models are proposed and combined into one for designing accurate and efficient course material recommendation model. In initial steps, popular datamining algorithms, i.e., K-Means, fuzzy c-means (FCM), and kernel-based FCM (K-FCM), are implemented to cluster students according to their learning behaviors. the comparative study demonstrates that K-FCM-based pattern identification is providing more accuracy with respect to other two algorithms. On the other hand to design a model for student performance prediction, two supervised classifiers, i.e., C4.5 and CART, are implemented. During experiments, we found that the C4.5 decision works well for student performance dataset and for predicting the performance. Using boththe components, a course study material recommendation model is proposed. thus an application is i
the number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features af...
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ISBN:
(纸本)9783031276088;9783031276095
the number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machinelearning classifier. therefore, in this paper, we proposed the use of association rules to select features from medical data, which result in dimensionality reduction of the input feature space. the selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. the conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machinelearning models in terms of accuracy of classification.
the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physica...
ISBN:
(纸本)9798400708879
the proceedings contain 56 papers. the topics discussed include: a need finding study with low-resourced language content creators;designing a voice-controlled dialogue system for workplace learning of routine physical workers;an interface design methodology for serving machinelearning models;lions out of bounds? reflections on digital technology and matristic design to address human-wildlife conflict;an integration model to enhance information systems administration and data sharing – a case of Namibian lower courts;unmasking trust: examining users’ perspectives of facial recognition systems in Mozambique;and investigating the efficacy of large language models in reflective assessment methods through chain of thoughts prompting.
Emotion detection and recognition from text is a recent field of research that is closely related to Sentiment analysis. Many people express themselves using text, photographs, music, and video. Text communication usi...
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the growing demand to identify potential bankrupt companies has prompted more research into bankruptcy prediction, assisting stakeholders in determiningthe worthiness of an investment. the Indian stock market offers ...
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Since AlphaGo beat the world Go champion in 2016, which attracted wide attention, the neural network has become more and more popular in recent years, and people's research on it has gradually improved and been ap...
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Withthe rapid development of digitalization, cloud platforms have become a key technology supporting remote networking. However, the accompanying cybersecurity issues, particularly the detection of anomalous access a...
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