Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

Çalışkan, Abidin

doi:10.1177/01423312221147335

Cited by 13 publications

(2 citation statements)

References 41 publications

(49 reference statements)

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“…The depths and number of layers of ResNet models differ from each other. As a common feature of ResNet models, the input dimensions have a resolution of 224 × 224 pixels, and the output layers are fully connected (FC) (Çalışkan, 2023; Yue et al, 2018). The pre-trained models ResNet-18, ResNet-50, and ResNet-101 were used for the experimental analysis of this study.…”

Section: Methodsmentioning

confidence: 99%

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Çalışkan

2023

Transactions of the Institute of Measurement and Control

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Minerals are inorganic substances that formed homogeneously by the combination of chemicals that comprise the Earth’s crust and mines are the commercially valuable natural minerals. Since minerals form as a result of chemical interactions in different natural environments, each may have different physical properties. Manually classifying minerals with the naked eye results in distinguishing issues, economic losses, and performance problems. In this study, a deep learning–based hybrid method for distinguishing seven mineral types is proposed. Deep learning models are used to combine the features extracted from residual blocks. The most inefficient features in the feature set were chosen using metaheuristic optimization. The complement rule was then used to combine inefficient features from the feature set into clusters. Consequently, efficient features were obtained, with an overall classification accuracy of 96.14%. The use of the complement rule method in the data set, along with the optimization methods, was shown to improve classification performance. This study is expected to help improve the efficiency and speed of mineral classification.

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Section: Methodsmentioning

confidence: 99%

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Çalışkan

2023

Transactions of the Institute of Measurement and Control

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“…Finally, the model is completed with a dense layer with a single output and utilises the linear activation function. Among various neural network architectures, CNNs are commonly employed for tasks such as image recognition, image classification, object detection, and facial recognition [36]. CNNs consist of neurons with trainable weights and biases, allowing them to capture and enhance low-level features in data.…”

Section: Artificial Neural Network Structurementioning

confidence: 99%

Prediction of Wind Power with Machine Learning Models

Karaman

2023

Applied Sciences

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Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (°), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R2 value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.

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Processing 2D barcode data with metaheuristic based CNN models and detection of malicious PDF files

Toğaçar,

Ergen

2024

Applied Soft Computing

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Diagnosis of malaria disease by integrating chi-square feature selection algorithm with convolutional neural networks and autoencoder network

Cited by 13 publications

References 41 publications

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Finding complement of inefficient feature clusters obtained by metaheuristic optimization algorithms to detect rock mineral types

Prediction of Wind Power with Machine Learning Models

Processing 2D barcode data with metaheuristic based CNN models and detection of malicious PDF files

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