Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Gadekallu, Thippa Reddy; Bhattacharya, Sweta; Maddikunta, Praveen Kumar Reddy; Hakak, Saqib; Khan, Wazir Zada; Bashir, Ali Kashif; Jolfaei, Alireza; Tariq, Usman

doi:10.1007/s11042-020-09988-y

Cited by 42 publications

(12 citation statements)

References 44 publications

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“…Artificial intelligence (AI) including DL has emerged recently though various applications in healthcare [7][8][9]. Efficient cancer characterization in BUS images can be obtained by appropriate automatic SS scheme [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

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Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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“…An imbalanced dataset causes machine learning algorithms to under-perform [14,18,28,31]. The synthetic minority oversampling technique (SMOTE) [4,15,16] is a powerful approach to tackle the class imbalance problem.…”

Section: Preprocessingmentioning

confidence: 99%

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Zehra

Javed

Jalil

et al. 2021

Complex Intell. Syst.

107

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Receiving an accurate emotional response from robots has been a challenging task for researchers for the past few years. With the advancements in technology, robots like service robots interact with users of different cultural and lingual backgrounds. The traditional approach towards speech emotion recognition cannot be utilized to enable the robot and give an efficient and emotional response. The conventional approach towards speech emotion recognition uses the same corpus for both training and testing of classifiers to detect accurate emotions, but this approach cannot be generalized for multi-lingual environments, which is a requirement for robots used by people all across the globe. In this paper, a series of experiments are conducted to highlight an ensemble learning effect using a majority voting technique for cross-corpus, multi-lingual speech emotion recognition system. A comparison of the performance of an ensemble learning approach against traditional machine learning algorithms is performed. This study tests a classifier’s performance trained on one corpus with data from another corpus to evaluate its efficiency for multi-lingual emotion detection. According to experimental analysis, different classifiers give the highest accuracy for different corpora. Using an ensemble learning approach gives the benefit of combining all classifiers’ effect instead of choosing one classifier and compromising certain language corpus’s accuracy. Experiments show an increased accuracy of 13% for Urdu corpus, 8% for German corpus, 11% for Italian corpus, and 5% for English corpus from with-in corpus testing. For cross-corpus experiments, an improvement of 2% when training on Urdu data and testing on German data and 15% when training on Urdu data and testing on Italian data is achieved. An increase of 7% in accuracy is obtained when testing on Urdu data and training on German data, 3% when testing on Urdu data and training on Italian data, and 5% when testing on Urdu data and training on English data. Experiments prove that the ensemble learning approach gives promising results against other state-of-the-art techniques.

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“…Changes in the closing price on the forecast date reveal how much the price rose or fell (after 1 day or 5 days) based on the last day of the 30 days after the image was created (see Eqs (1), ( 2)). If the image name rate of return was greater than 0, 1 was automatically added to the (3,6,12), (4,8,16), (5,10,20), (6,12,24), (7,14,28), (8,16,32), (9,18,36) (10,20,40), (11,22,44), (12,24,48), (13,26,52), (14,28,56), (15,30,60), (16,32,64) https://doi.org/10.1371/journal.pone.0253121.t003…”

Section: Datamentioning

confidence: 99%

“…Our experiments identify which factors affect prediction accuracy and which characteristics of charts are useful to increase that accuracy. We hope that our results may be useful for deep learning practitioners to select optimal hyperparameters in a minimal amount of time for the purpose of stock price prediction [15].…”

Section: Introductionmentioning

confidence: 96%

Impact of chart image characteristics on stock price prediction with a convolutional neural network

Jin

Kwon

2021

PLoS ONE

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Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.

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Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Abstract: Usman (2020) Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset. Multimedia Tools and Applications.

Cited by 42 publications

References 44 publications

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Cross corpus multi-lingual speech emotion recognition using ensemble learning

Impact of chart image characteristics on stock price prediction with a convolutional neural network

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