Perancangan Jaringan Local Area Network sebagai Monitoring Pembelajaran di Laboratorium Komputer

With the rapid development of information technology in modern society, the application of multimedia integration platform is more and more extensive. Speech recognition has become an important subject in the process of multimedia visual interaction. The accuracy of speech recognition is dependent on a number of elements, two of which are the acoustic characteristics of speech and the speech recognition model. Speech data is complex and changeable. Most methods only extract a single type of feature of the signal to represent the speech signal. This single feature cannot express the hidden information. And, the excellent speech recognition model can also better learn the characteristic speech information to improve performance. This work proposes a new method for speech recognition in multimedia visual interaction. First of all, this work considers the problem that a single feature cannot fully represent complex speech information. This paper proposes three kinds of feature fusion structures to extract speech information from different angles. This extracts three different fusion features based on the low-level features and higher-level sparse representation. Secondly, this work relies on the strong learning ability of neural network and the weight distribution mechanism of attention model. In this paper, the fusion feature is combined with the bidirectional long and short memory network with attention. The extracted fusion features contain more speech information with strong discrimination. When the weight increases, it can further improve the influence of features on the predicted value and improve the performance. Finally, this paper has carried out systematic experiments on the proposed method, and the results verify the feasibility.

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Medical image data augmentation: techniques, comparisons and interpretations

Göçeri

2023

Artif Intell Rev

114

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Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.

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Perancangan Jaringan Local Area Network sebagai Monitoring Pembelajaran di Laboratorium Komputer

Cited by 12 publications

References 7 publications

Predictability of Metaverse Coins Using an Advanced Machine Learning Approach

Predictability of Metaverse Coins Using an Advanced Machine Learning Approach

Intelligent speech recognition algorithm in multimedia visual interaction via BiLSTM and attention mechanism

Medical image data augmentation: techniques, comparisons and interpretations

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