BiLSTM deep neural network model for imbalanced medical data of IoT systems

Woźniak, Marcin; Wieczorek, Michał; Siłka, Jakub

doi:10.1016/j.future.2022.12.004

Cited by 92 publications

(12 citation statements)

References 18 publications

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“…To ensure the security of the data shared across the unsecured channel using the socket layer (SSL), the data's privacy is enforced through two‐factor authentication. MongoDB, a NoSQL database, stores the corresponding user data 46,47 . If the technology described above were implemented, the future view paradigm would be replaced with a user‐centric system that includes all the essential components.…”

Section: Experimentation Results and Analysismentioning

confidence: 99%

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XAI‐driven model for crop recommender system for use in precision agriculture

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

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Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1‐score. In contrast to other cutting‐edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1‐score by 10%.

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Section: Experimentation Results and Analysismentioning

confidence: 99%

“…MongoDB, a NoSQL database, stores the corresponding user data. 46,47 If the technology described above were implemented, the future view paradigm would be replaced with a user-centric system that includes all the essential components.…”

Section: Practical Implicationsmentioning

confidence: 99%

XAI‐driven model for crop recommender system for use in precision agriculture

Naga Srinivasu,

Ijaz,

Woźniak

2024

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

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“…From their experimental result, they found 99% accuracy over binary classification and 98% for multiclass classification using the VGG19 model. Wozniak et al [29] have provided a Bidirectional Long Short-Term Memory Network model for the prediction of the health status of cervical cancer patients with an accuracy of 96%, precision above 88%, and recall above 96%. Many works have been reported for BC detection from mammogram images using ML and DL techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification

Pal,

Parija,

Panda

et al. 2024

Preprint

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Breast cancer (BC) is the most frequently diagnosed cancer among women, surpassing all other types of cancer in terms of prevalence. It affects both males and females, but women are at a greater risk of developing it. The lifetime probability of developing breast cancer for women is approximately 1 in 38. The focus of this study is to differentiate between benign and malignant breast cancer tumors using the fine needle aspiration (FNA) signal as the primary source of information. Four deep learning (DL) models, namely long short-term memory (LSTM), Gated recurrent unit (GRU), Deep belief network (DBN), and autoencoder (AE) have been utilized to achieve this goal. The proposed models have been trained and validated using two public breast cancer datasets: the Wisconsin Original Breast Cancer dataset (WBC) and the Wisconsin Diagnostic Breast Cancer dataset (WDBC). To establish a reliable model, three different types of training techniques have been utilized, including the 80:20 split, the 70:30 split, and the k-fold method. The experimental investigation incorporated three different data characteristics, namely balanced, less imbalanced, and extremely imbalanced data. The simulation-based experimental findings indicate that the LSTM model achieves high levels of accuracy, F1-score, and area under the curve (AUC) when applied to the two commonly used datasets. The WDBC dataset yields accuracy, F1-score, and AUC values of 0.98, 0.98, and 0.99, respectively, while the WBCD dataset yields values of 0.99, 0.99, and 1, respectively. These results were obtained using a 3-fold training scheme and balanced data. The LSTM model consistently outperforms the other three models, regardless of variations in datasets, training methods, and changes in data properties. The efficacy of the models can be evaluated by subjecting the deep learning models to bigger and varying degrees of unbalanced data samples, including both balanced and less skewed datasets. To further this study, we aim to explore the effectiveness of DL models in conjunction with an IoT system to improve breast cancer detection accuracy in online mode for patients residing in remote areas.

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“…Deep learning based methods have the capability to capture fine details of visual data [38], due to huge weighted layers of features representation [39], [40]. However, deep learning based approaches need extensive computations and huge amounts of data for training the model from the scratch.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

Ahmad

Alwageed

et al. 2023

IEEE Access

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Recurrent Neural Networks (RNNs) and their variants have been demonstrated tremendous successes in modeling sequential data such as audio processing, video processing, time series analysis, and text mining. Inspired by these facts, we propose human activity recognition technique to proceed visual data via utilizing convolution neural network (CNN) and Bidirectional-gated recurrent unit (Bi-GRU). Firstly, we extract deep features from frames sequence of human activities videos using CNN and then select most important features from the deep appearances to improve performance and decrease computational complexity of the model. Secondly, to learn temporal motions of frames sequence, we design Bi-GRU and feed those deep-important features extracted from frames sequence of human activities to Bi-GRU which learn temporal dynamics in forward and backward direction at each time step. We conduct extensive experiments on realistic videos of human activity recognition datasets YouTube11, HMDB51 and UCF101. Lastly, we compare the obtained results with existing methods to show the competence of our proposed technique.

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BiLSTM deep neural network model for imbalanced medical data of IoT systems

Cited by 92 publications

References 18 publications

XAI‐driven model for crop recommender system for use in precision agriculture

XAI‐driven model for crop recommender system for use in precision agriculture

Investigation on Effects of Training Schemes and Data Characteristics on Deep Learning-based Breast Cancer Classification

Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

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